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Understanding
   Color
Giordano Beretta
Hewlett-Packard Laboratories




http://www.inventoland.net/imaging/uc/

                    Alexandria 2008
Course objectives                                                                             1

•     Develop a systematic understanding of the principles of color
      perception and encoding
•     Understand the differences between the various methods for
      color imaging and communication
•     Gain a more realistic expectation from color reproduction
•     Develop an intuition for
      •        trade-offs in color reproduction systems
      •        interpreting the result of a color measurement




G.B. Beretta                        Alexandria, 6 June 2008     SC076 — Understanding Color
What is color?                                                                                    2
•     Color is an illusion
•     Colorimetry: the art to predict an illusion from a physical
      measurement
•     Experience is much more important than knowing facts or
      theories
•     The physiology of color vision is understood only to a very small
      degree
      •        Physiology: physical stimulus → physiological response
      •        Psychophysics: physical stimulus → behavioral response




      What is essential is invisible to the eye
      Antoine de Saint-Exupéry (The Little Prince)




G.B. Beretta                              Alexandria, 6 June 2008   SC076 — Understanding Color
1              Terminology                                                                     3
               CIE definition 845-02-18: (perceived) color

Attribute of a visual perception consisting of any combination of chromatic and
achromatic content. This attribute can be described by chromatic color names such
as yellow, orange, brown, red, pink, green, blue, purple, etc., or by achromatic color
names such as white, gray, black, etc., and qualified by bright, dim, light, dark etc.,
or by combinations of such names
Perceived color depends on the spectral distribution of the color stimulus, on the
size, shape, structure and surround of the stimulus area, on the state of adaptation
of the observer’s visual system, and on the observer’s experience of the prevailing
and similar situations of observation
Perceived color may appear in several modes of appearance.
The names for various modes of appearance are intended to
distinguish among qualitative and geometric differences of
color perceptions




G.B. Beretta                    Alexandria, 6 June 2008          SC076 — Understanding Color
1.0.1 Color term categories                                                      4
Subjective color term: A word used to describe a color attribute
perceived by a human. Example: the colorfulness of a flower
Objective color term: A word used to describe a physical quantity
related to color that can be measured. Example: the energy radiated
by a source
We use objective color terms as correlates to subjective color terms




G.B. Beretta            Alexandria, 6 June 2008    SC076 — Understanding Color
1.0.2 Subjective color terms — Hue                                                5
Hue: The attribute of a color perception denoted by blue, green,
yellow, red, purple, and so on


                   hue scale




Unique hue: A hue that cannot be further
described by use of the hue names other than
its own. There are four unique hues, each of
which shows no perceptual similarity to any of
the others: red, green, yellow, and blue




G.B. Beretta              Alexandria, 6 June 2008   SC076 — Understanding Color
1.0.3 Brightness                                                                    6

Brightness: The attribute of a visual sensation according to which a
given visual stimulus appears to be more or less intense, or according
to which the visual stimulus appears to emit more or less light
Objective term: luminance (L)




                  brightness scale




G.B. Beretta                Alexandria, 6 June 2008   SC076 — Understanding Color
1.0.4 Lightness                                                                      7

Lightness: The attribute of a visual sensation according to which the
area in which the visual stimulus is presented appears to emit more
or less light in proportion to that emitted by a similarly illuminated
area perceived as a “white” stimulus
Objective terms: luminance factor (β), CIE lightness (L*)


•     Brightness is absolute, lightness is relative to an area perceived
      as white




G.B. Beretta                Alexandria, 6 June 2008    SC076 — Understanding Color
1.0.5 Colorfulness                                                                 8

Chromaticness or Colorfulness: The attribute of a visual sensation
according to which an area appears to exhibit more or less of its hue.
In short: the extent to which a hue is apparent
Objective term: CIECAM02 M




G.B. Beretta             Alexandria, 6 June 2008     SC076 — Understanding Color
1.0.5.1 Colorfulness — Chroma                                                        9

Chroma: The attribute of a visual sensation which permits a
judgement to be made of the degree to which a chromatic stimulus
differs from an achromatic stimulus of the same brightness
In other words, chroma is an attribute orthogonal to brightness: absolute
colorfulness; we perceive a color correctly independently of the
illumination level

Objective term: CIE chroma (C*uv, C*ab)




G.B. Beretta              Alexandria, 6 June 2008      SC076 — Understanding Color
1.0.5.2 Colorfulness — Saturation                                                   10
Saturation: The attribute of a visual sensation which permits a
judgement to be made of the degree to which a chromatic stimulus
differs from an achromatic stimulus regardless of their brightness
In other words, it is the colorfulness of an area judged in proportion to its
brightness: relative colorfulness; we can judge the uniformity of an object’s
color in the presence of shadows and independently of the incident light’s
angle

Objective terms: purity (p), CIE saturation (Suv)


                    saturation scale




Colorfulness is absolute, chroma is relative to a white area and absolute
w.r.t. brightness, saturation is in proportion to brightness

G.B. Beretta                  Alexandria, 6 June 2008     SC076 — Understanding Color
1.1 Our goal                                                                                    11
•     We would like to be able to predict the color of a sample by
      making a measurement
•     Humans can distinguish about 7 to 10 million different colors —
      just name them and build an instrument that identifies them
•     Task: find good correlates to the subjective color terms
•     Some observations:
      •        If you want to buy a skirt or a pair of slacks to match a jacket, you cannot
               match the color by memory — you have to take the jacket with you
      •        Just matching in the store light is insufficient, you have to match also
               under the incandescent light in the dressing room and outdoors
      •        You always get the opinion of your companion or the store clerk

•     Three fundamental components of measuring color:
      •        light sources
      •        samples illuminated by them
      •        observers


G.B. Beretta                         Alexandria, 6 June 2008          SC076 — Understanding Color
1.2 Spectral curves                                                                                          12
               quantities we can measure

•     The spectral power curve gives at each wavelength the power (in watts), i.e.,
      the rate at which energy is received from the light source
•     The spectral reflectance curve gives at each wavelength the percentage of
      incident light that is reflected

                                    0.40




                                            reflectance
                                                          human complexion
                                    0.35

                                    0.30

                                    0.25

                                    0.20

                                    0.15

                                    0.10

                                    0.05

                                    0.00
                                             400             450    500      550     600      650      700 nm

G.B. Beretta                   Alexandria, 6 June 2008                             SC076 — Understanding Color
1.2.1 Spectral color reproduction                                                13
•     By spectral color reproduction we intend the physically correct
      reproduction of color, i.e., the duplication of the original
      object's spectrum
•     The general reproduction methods (micro-dispersion and
      Lippmann) are too impractical for normal use
•     For some special applications like painting restoration or
      illuminant reconstruction, the spectrum may be sampled at a
      small number of intervals and combined with principal
      component analysis
•     Fortunately, spectral color reproduction is required only in rare
      cases, such as paint swatches in catalogs, and in this cases it is
      often possible to use identical dyes
Our aim is to achieve a close effect for a normal viewer under average
viewing conditions
Mathematically: build a simple model of color vision

G.B. Beretta                Alexandria, 6 June 2008    SC076 — Understanding Color
2              Color theories                                                                14
•     800 B.C.E. — Indian Upanishads
      •        there are relations among colors

•     400 B.C.E. — Hellenic philosophers
      •        Plato: light or fire rays emanate from the eyes
      •        Epicurus: replicas of objects enter the eyes

•     First Millennium — Arab school, pure science
      •        Abu Ali Mohammed Ibn al Hazen: image is formed
               within the eye like in a camera obscura

•     15th century — Renaissance, technology
      •        Leonardo da Vinci:
               • color perception
               • color order system
               • black & white are colors
               • 3 pairs of opponent colors (black–white, red–green, yellow–blue)
               • simultaneous contrast
               • used color filters to determine color mixtures



G.B. Beretta                        Alexandria, 6 June 2008        SC076 — Understanding Color
2.0.1 Opponent colors                                                                                15
                   W
      Y
                                R
G                                                         W



                         B                                     Y

                                                                                         Y
                 K
                                     G
                                                                   R
                                               B

                                                                                       W

                                                   K
                                                                       G                             R



                                                                                         B

      Note: rendered with chiaro-scuro technique



G.B. Beretta                             Alexandria, 6 June 2008           SC076 — Understanding Color
2.0.2 Color theories (cont.)                                                                    16

•     18th century — Enlightenment, physics & chemistry
      •        Isaac Newton:
               • spectral dispersion, white can be dispersed in a spectrum by a prism
               • colors of objects relate to their spectral reflectance
               • light is not colored and color perception is elicited in the human visual
                  system

•     19th century — scientific discovery
      •        Thomas Young: trichromatic theory
      •        Hermann von Helmholtz: spectral sensitivity curves
      •        Ewald Hering:
               • opponent color theory (can explain hues, saturation, and why there is
                 no reddish green or yellowish blue)
               • black and dark gray are not produced by the absence of light but by a
                 lighter surround




G.B. Beretta                         Alexandria, 6 June 2008          SC076 — Understanding Color
2.0.3 Color theories (cont.)                                                                  17

•     20th century — advanced scientific instruments
      •        Johannes A. von Kries: chromatic adaptation
               • why is white balance necessary?
      •        Georg Elias Müller & Erwin Schrödinger: zone theory
      •        physiological evidence for inhibitory mechanisms becomes available in the
               1950s
      •        molecular biology
      •        functional MRI techniques
      •        see http://webvision.med.utah.edu/ for the latest progress




G.B. Beretta                        Alexandria, 6 June 2008         SC076 — Understanding Color
2.1 Color vision is not based on a bitmap 18
•     Vision is based on contrast
•     Vision is not hierarchical. The simple model
                                           distal event
                                                 ↓
                                        proximal stimulus
                                                 ↓
                                           brain event
      is very questionable. It is believed that feedback loops exist between all 26
      known areas of visual processing
•     In fact, it has been proved that a necessary condition of some activity in even
      the primary visual cortex is input from “higher” areas
•     Like the other sensory systems, vision is narcissistic
•     Many sensory signals are non-correlational — a given signal does not always
      indicate the same property or event in the world
The “inner eye’s” function is not to understand what the sensory
states indicate
      Science 17 March 2006: Vol. 311. no. 5767, pp. 1606 - 1609


G.B. Beretta                            Alexandria, 6 June 2008    SC076 — Understanding Color
2.1.1 Cognitive model for color appearance                                                                             19
     stimulus           detectors   early mechanisms             pictorial register

                                                           color
                                                            edges
                                                           contour
                                                           motion
                                                            depth
                                                             …
                                                                              context parameters

                                                                                                         chroma
                                                                                                  etc.
                                                                                                                 hue
                                    Color lexicon                                                    lightness

                                                                            chroma                 internal
                                                                     etc.
                                                                                                  color space
                                      amber                                         hue
                                                                        lightness

               action               color name                     apparent color
                                                                   representation

•     Reliable color discrimination: 1 week
•     Color-opponent channels: 3 months
•     Color constancy: 4 months
•     Internal color space
•     Color names
G.B. Beretta                           Alexandria, 6 June 2008                            SC076 — Understanding Color
2.1.2 Memory colors                                                                              20
•     Vision is not hierarchical
•     Delk & Fillenbaum experiment (1965)




•     We tend to see colors of familiar objects as we expect them to be
                                                                                          Surround
                                                                10º
                                                                             Sky
                                                                                       Complexion


                                                                        2º
                                                             Adapting
                                                               field
                                                                                   Vegetation




G.B. Beretta                   Alexandria, 6 June 2008           SC076 — Understanding Color
2.2 Color vision physiology                                                                                                 21
•     The retina has a layer of photoreceptors, which grow like hair (10μm per day).
      They are of two kinds: rods and cones
•     The cones are of three kinds, depending on the pigments they contain. One
      pigment absorbs reddish light, one absorbs greenish light, and one absorbs
      bluish light
•     This leads to the method of trichromatic color reproduction, in which we try to
      stimulate independently the three kinds of cones
                                                                    ls
                                                  s             cel                                                        um
                                            ib er
                                                         lio
                                                             n
                                                                         ls                        ll s ells        th eli
                                      rvef            ng             cel          ce lls
                                                                                            ta l ce ne c ones t epi
                                    ne             ga            ne                       on & co s & c men
                                tic          in al         a cri            o lar      riz d
                              op         ret          am               bip         ho ro          rod pig


stimulus




G.B. Beretta                   Alexandria, 6 June 2008                                    SC076 — Understanding Color
2.2.1 Photoreceptors                                                                                         22




G.B. Beretta                            Alexandria, 6 June 2008                    SC076 — Understanding Color
      Credit: Carlos Rozas (CanalWeb, Chile) http://webvision.med.utah.edu/movies/3Drod.mov
2.2.1.1 Outer segment                                                                                                  23




          http://webvision.med.utah.edu/movies/discs.mov




                                                           http://webvision.med.utah.edu/movies/phago4.mov




      Credit: Helga Kolb

G.B. Beretta                                 Alexandria, 6 June 2008                         SC076 — Understanding Color
2.2.2 Evolution                                                                                                24
•      From the difference in the amino-acid sequences for the various
       photoreceptor genes it is clear that the human visual system did not evolve
       according to a single design
                                  Finding                     Rod and S Mechanisms      L and M Mechanisms

                                Distribution                           perifoveal              foveal
        Anatomy                                                        one class             two classes
                              Bipolar circuitry
                                                                       (only on)            (on and off)
                             Spatial resolution                           low                  high
                            Temporal resolution                           low                  high
     Psychophysics
                              Weber fraction                             high                   low
                           Wavelength sensitivity                        short                medium
                             Response function                         saturates         does not saturate
                                 Latencies                               long                  short
                               ERG-off-effect                          negative               positive
    Electrophysiology
                          Ganglion cell response                  afterpotential         no afterpotential
                              Receptive field                             large                 small
                               Vulnerability                             high                   low

        Genetics                                                       autosomal             sex-linked

       Source: Eberhart Zrenner, 1983


G.B. Beretta                                 Alexandria, 6 June 2008                 SC076 — Understanding Color
2.2.3 Catching photons                                                                                                                                25
•     Retinal pigments: rhodopsin, cyanolabe, chlorolabe, erythrolabe
      •        lysine attaches chromophore to a protein backbone
      •        electronic excitation (two-photon catch) initiates a large shift in electron
               density in less than 10–15 seconds
      •        shift activates rotation around two double-bonded carbon atoms in the
               backbone
      •        entire photocycle lasts less than a picosecond (10–12 sec.)
      •        photoisomerization induces shift in positive charge perpendicular to
               membrane sheets containing the protein
      •        this generates a photoelectric signal with a less than 5 psec. rise time
      •        forward reaction is completed in ~50 μsec. (10–6 sec.)

•     Quantum efficiency: measure of the probability                           S harpe et al. ∑Human R ed, G reen, and R ed-G reen Hybrid C one P igments




      that the reaction will take place after the
      absorption of a photon of light
•     4 pigments sensitized to photons at 4 energy levels
      (wavelength): L, M, S, and rods


G.B. Beretta                         Alexandria, 6 June 2008           SC076 — Understanding Color
2.2.4 Phototransduction                                                                               26




      Credit: Helga Kolb,http://webvision.med.utah.edu/movies/trasduc.mov



G.B. Beretta                            Alexandria, 6 June 2008             SC076 — Understanding Color
2.2.5 Catch probabilities                                                                                                        27
•     Quantum energy of a photon: hν
•     For each pigment, there is a probability distribution for a reaction, depending
      on the photon’s wavelength
•     w(λ) dλ
•     What counts is not the energy of a single photon, but the average
•     For a spectral power distribution Pλ:
      S = ∫ Pλ w(λ) dλ
 absorbance
                                                                                 S-cone
1.0
                                                                                 M-cone
0.8
                                                                                 L-cone

0.6                                                                              Rod

0.4

0.2

                                                                                   nm
0.0
           400          450          500           550          600          650
      Dartnall, H. J. A., Bowmaker, J. K., & Mollon, J. D. (1983). Human visual pigments: microspectrophotometric results from
      the eyes of seven persons. Proceedings of the Royal Society of London, B 220, 115-130

G.B. Beretta                                   Alexandria, 6 June 2008                               SC076 — Understanding Color
2.2.6 Retinal mechanisms                                                                                      28
                                                                         Surround


                                                                         Center


                                                                         Surround




                        Retinal Amacrine Bipolar Horizontal   Receptor
                       ganglion   cell     cell     cell
                         cell


•     Receptors in retina are not like pixels in a CCD
•     Receptive field: area of visual field that activates a retinal ganglion
      (H.K. Hartline, 1938)
•     Center-surround fields allow for adaptive coding (transmit contrast instead of
      absolute values)
•     Horizontal cells presumed to inhibit either its bipolar cell or the receptors:
      opponent response in red–green and yellow–blue potentials (G. Svaetichin,
      1956)
•     Balance of red–green channel might be determined by yellow
•     Retinal ganglion can be tonic or phasic: pathway may also be organized by
      information density or bandwidth

G.B. Beretta                         Alexandria, 6 June 2008                        SC076 — Understanding Color
2.2.7 Parvocellular and magnocellular pathways                                                                                                   29
                                                              P–                                                      M–

 Originating retinal ganglion cells                          Tonic                                                  Phasic

                                                                                             Fast (mostly transient responses, some sustained,
        Temporal resolution           Slow (sustained responses, low conduction velocity)
                                                                                                         high conduction velocity)

                                                          Chromatic                                               Luminance
      Modulation dominance
                                            Adaptation occurs at high frequencies                   Adaptation occurs at all frequencies

                                                                                               Receives mostly combined (broadband) input
                                       Receives mostly opponent type input from cones
               Color                                                                          from M and L cones, both from the center and
                                           sensitive to short and long wavelengths
                                                                                                  from the surround of receptive fields

        Contrast sensitivity                        Low (threshold > 10%)                                   High (threshold < 2%)

        LGN cell saturation                    Linear up to about 64% contrast                                      At 10%

         Spatial resolution                            High (small cells)                                      Low (large cells)

                                      When fixation is strictly foveal, extraction of high
                                       spatial frequency information (test gratings),                        Responds to flicker
    Spatio-temporal resolution             reflecting small color receptive fields
                                                                                                            Short integration time
                                                    Long integration time
                                      Could be a site for both a lightness channel as for     Might be a site for achromatic channels because
                                      opponent-color channels. The role depends on the        the spectral sensitivity is similar to Vλ, it is more
       Relation to channels
                                      spatio-temporal content of the target used in the      sensitive to flicker, and has only a weak opponent
                                                          experiment                                          color component

  Possible main role in the visual    Sustain the perception of color, texture, shape, and    Sustain the detection of movement, depth, and
             system                                     fine stereopsis                                             flicker




G.B. Beretta                                    Alexandria, 6 June 2008                                     SC076 — Understanding Color
2.2.8 Color constancy                                                                                  30




                  Optic
                  tract    Lateral                          Primary   Blob
                          geniculate                         visual
                                         Optic               cortex
                            body
                                       radiations


•     Axons of retinal ganglion cells in optical nerve terminate at LGN and synapse
      with neurons radiating to striate cortex
•     LGN might generate masking effects; combination with saccadic motion of eye
•     Blobs in area 17 consist mainly of double opponent cells
•     May be site for color constancy
•     Requires input from V4 (Zeki)
Why is white balancing necessary in color reproduction?


G.B. Beretta                            Alexandria, 6 June 2008              SC076 — Understanding Color
2.3 Limited knowledge                                                            31

•     Reaction time at rhodopsin level: femtoseconds
•     Reaction time at perceptual level: seconds
•     From photon catches to constant color names
               We do not know exactly what happens in-between
•     Examples: simultaneous contrast, chromatic induction




G.B. Beretta                 Alexandria, 6 June 2008   SC076 — Understanding Color
2.3.1 1 color appears as 2                                                              32




               Appearance mode




Three flat objects or picture of a white cube illuminated from the top and right?
G.B. Beretta                  Alexandria, 6 June 2008         SC076 — Understanding Color
2.4 Basis for colorimetry                                                               33
•     Too many unknowns in physiology and cognitive processes
•     Cannot yet build accurate color vision model
•     Unlike auditory system, visual system is not spectral but
      integrative
      •        Advantage of integrative system: metamerism

•     Basis of colorimetry:

1. Instead of a physiological model, build a psychophysical model
      •        Physiology:
               physical stimulus → physiological response
      •        Psychophysics:
               physical stimulus → behavioral response
2. Assume additivity
3. Keep the viewing conditions constant



G.B. Beretta                        Alexandria, 6 June 2008   SC076 — Understanding Color
3              Colorimetry                                                     34
Colorimetry is the branch of color science concerned with specifying
numerically the color of a physically defined visual stimulus in such a
manner that:

1. when viewed by an observer with normal color vision, under the
    same observing conditions, stimuli with the same specification
    look alike,
2. stimuli that look alike have the same specification, and
3. the numbers comprising the specification are functions of the
    physical parameters defining the spectral radiant power
    distribution of the stimulus
Trichromatic generalization: over a wide range of conditions of
observation, many color stimuli can be matched in color completely
by additive mixtures of three fixed primary stimuli whose radiant
powers have been suitably adjusted (proportionality). In addition,
the color stimuli combine linearly, symmetrically, and transitively
Grassmann’s laws of additive color mixture
G.B. Beretta             Alexandria, 6 June 2008     SC076 — Understanding Color
3.1 Color matching                                                         35
Colors are assessed by matching them with reference colors on a
small-field bipartite screen:




G.B. Beretta           Alexandria, 6 June 2008   SC076 — Understanding Color
3.1.1 Color-matching functions                                                                     36
Given a monochromatic stimulus Qλ of wavelength λ, it can be written as
       Qλ = RλR + GλG + BλB,
where Rλ, Gλ, and Bλ are the spectral tristimulus values of Qλ
       Assume an equal-energy stimulus E whose mono-chromatic constituents are Eλ
       (equal-energy means Eλ ≡ 1)
The equation for a color match involving a mono-chromatic constituent Eλ
of E is
       Eλ = r(λ)R + g(λ)G + b(λ)B,
where r(λ), g(λ), and b(λ), are the spectral tristimulus values of Eλ. The sets
of such values are called color-matching functions
3.0
                                              Stiles-Burch (1955;1959)
2.5
2.0                                                              b(λ)
1.5                                                              g(λ)
1.0                                                              r(λ)
0.5
0.0
                                                               nm
-0.5
       400          500          600                     700

G.B. Beretta                         Alexandria, 6 June 2008             SC076 — Understanding Color
3.1.2 Metameric stimuli                                                              37
Consider two color stimuli
      Q1 = R1R + G1G + B1B
      Q2 = R2R + G2G + B2B
                                0.6




                                       reflectance
If Q1 and Q2 have
different spectral radiant      0.5
                                                                 D
power distributions, but                                         C
R1 = R2 and G1 = G2 and B1      0.4                              B

= B2, the two stimuli are                                        A

called metameric stimuli        0.3


•     Color reproduction        0.2
      works because of
      metamerism                0.1

                                                                                    nm

                                0.0
                                      400            500   600               700


G.B. Beretta               Alexandria, 6 June 2008         SC076 — Understanding Color
3.1.2.1 Kinds of metamerism                                                                  38

•     Illuminant metamerism
      •        example: daylight and a D65 simulation fluorescent lamp

•     Object metamerism
      •        example: metameric inks (see metamerism kit)

•     Sensor metamerism
      •        example: scanner and human visual system

•     Complex metamerism
      •        example: two inks metameric under two illuminants




G.B. Beretta                       Alexandria, 6 June 2008         SC076 — Understanding Color
3.2 Chromaticity diagrams                                                                                                    39
We can normalize the color-matching functions and thus obtain new
quantities
      r (λ) = r (λ) / [r (λ) + g(λ) + b(λ)]
      g(λ) = g(λ) / [r (λ) + g(λ) + b(λ)]
      b(λ) = b(λ) / [r (λ) + g(λ) + b(λ)]

with r(λ) + g(λ) + b(λ) = 1                                                     2.0


                                                                                          g(m)
The locus of chromaticity points                                                1.5

for monochromatic colors so
determined is called the spectrum                                               1.0                  2° pilot group
                                                                                                     Stiles-Burch (1955)
locus in the (r, g)-chromaticity
diagram                                                                         0.5



                                                                                                                           r(m)
                                                                                       0.0
                                                    -1.2 -1.0 -0.8 -0.6 -0.4 -0.2   0.0      0.2   0.4   0.6   0.8   1.0     1.2


                                                                                -0.5


G.B. Beretta                   Alexandria, 6 June 2008                              SC076 — Understanding Color
3.2.1 Imaginary color stimuli                                                                    40
•     The fact that the color-matching functions and the chromaticity coordinates
      can be negative presents a problem when the tristimulus values are computed
      from a spectral radiant power distribution
•     Because the color-
      matching space is                                                  spectrum locus
      linear, a linear
      transformation can 2.0
      be applied to the
      primary stimuli to                                                          A: ~2856˚K
      obtain new            1.5                              Planckian locus
      imaginary stimuli                                                    D65: ~6504˚K

      that lie outside the                                          ∞
      chromaticity region 1.0
      bounded by the
      spectrum locus. This
      ensures that the
                            0.5                                                  z2(λ)
      chromaticity                                                               y2(λ)
      coordinates are                                                            x2(λ)
      never negative                                                                        nm
                              0.0
                                      400              500    600        700            800


G.B. Beretta                        Alexandria, 6 June 2008            SC076 — Understanding Color
3.3 CIE 1931 standard colorimetric                                                                  41
  observer
We want to obtain results valid for the group of normal trichromats
(95% of population)


Because
      R = ∫ P λ r (λ ) d λ ,   G = ∫ Pλ g(λ) dλ,             B = ∫ Pλ b(λ) dλ,

an ideal observer can be defined by specifying values for the color-
matching functions
The Commission Internationale de l'Éclairage (CIE) has
recommended such tables containing x(λ), y(λ), z(λ)
for λ ∈ [360 nm, 830 nm] in 1 nm steps



G.B. Beretta                       Alexandria, 6 June 2008                SC076 — Understanding Color
3.3.0.1 CIE 1931 Observer (cont.)                                              42
In addition to the color-matching properties, the CIE 1931 Standard
Observer is such that it has also the heterochromatic brightness-
matching properties. The latter is achieved by choosing y (λ) to
coincide with the photopic luminous efficiency function


      X and Z are on the alychne, which in the chromaticity diagram is
      a straight line on which are located the chromaticity points of
      all stimuli having zero luminance

The data is based averaging the results a) on color matching in a 2°
field of 17 observers and b) the relative luminances of the colors of
the spectrum, averaged for about 100 observers




G.B. Beretta               Alexandria, 6 June 2008   SC076 — Understanding Color
3.4 Tristimulus normalization                                                                                       43

•     X, Y, and Z are defined up to a common normalization factor. This factor is
      different for objects and for emissive sources
•     The perfect reflecting diffuser is an ideal isotropic diffuser with a reflectance
      equal to unity
•     The perfect reflecting diffuser is completely matt and is entirely free from any
      gloss or sheen. The reflectance is equal to unity at all wavelengths
•     When the tristimulus values are measured with an instrument, YL represents a
      photometric measure, such as luminance. For object surfaces it is customary to
      scale X, Y, Z, so that Y = 100 for the perfect diffuser
      In practice a working standard such as a BaSO4 plate or a ceramic tile is used in lieu of the perfect
      diffuser


•     For emissive sources there is no illuminant and therefore the perfect diffuser is
      not relevant. So it is customary to use the photometric measures




G.B. Beretta                               Alexandria, 6 June 2008                        SC076 — Understanding Color
4              Objective color terms                                                44
               quantities we can measure

Dominant wavelength: Wavelength of the monochromatic stimulus
that, when additively mixed in suitable proportions with a specified
achromatic stimulus, matches the color stimulus considered
(In disuse, replaced by chromaticity)

Luminance: The luminous intensity in a given direction per unit projected
area
      L v = K m ∫ L e, λ V ( λ ) dλ
                    λ

where Km is the maximum photopic luminous efficacy (683 lm W–1), Le,λ the
radiance, and V(λ) the photopic efficiency

Luminance factor: The ratio of the luminance of a color to that of a
perfectly reflecting or transmitting diffuser identically illuminated
Symbol: β

G.B. Beretta                    Alexandria, 6 June 2008   SC076 — Understanding Color
4.1 Y                                                                         45

Y stimulus (luminosity in some literature): In the XYZ system the
luminance depends entirely on the Y stimulus. The Y values of any
two colors are proportional to their luminances. Therefore, Y gives
the percentage reflection or transmission directly, where a perfectly
reflecting diffuser or transmitting color has a value of Y = 100
      Y = V
where V is the luminance of the stimulus computed in accordance
with the luminous efficiency function V(λ)


Application: conversion of a color image to black and white




G.B. Beretta             Alexandria, 6 June 2008    SC076 — Understanding Color
4.1.1 Excitation purity                                                                           46

Excitation purity: A measure of the proportions of the amounts of
the monochromatic stimulus and of the specified achromatic
stimulus that, when additively mixed, match the color stimulus
considered
(In disuse, replaced by chromaticity)
             x – xw                                 y – yw
      p c = ------------------   or          p c = ------------------
            xb – xw                                yb – yw
where w denotes the achromatic stimulus and b the boundary color
stimulus




G.B. Beretta                      Alexandria, 6 June 2008               SC076 — Understanding Color
4.1.2 Chromaticity                                                                                                                           47

Chromaticity: Proportions of the amounts of three color-matching stimuli
needed to match a color (see p. 39).
Relationship between chromaticity coordinates r(λ), g(λ), b(λ) and x(λ), y(λ),
z(λ) of a given spectral stimulus of wavelength λ are expressed by the
projective transformation

                0.49000r ( λ ) + 0.31000g ( λ ) + 0.20000b ( λ )
      x ( λ ) = ----------------------------------------------------------------------------------------------------------
                                                                                                                         -
                0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ )
                0.17697r ( λ ) + 0.81240g ( λ ) + 0.01063b ( λ )
      y ( λ ) = ----------------------------------------------------------------------------------------------------------
                                                                                                                         -
                0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ )
                0.00000r ( λ ) + 0.01000g ( λ ) + 0.99000b ( λ )
      z ( λ ) = ----------------------------------------------------------------------------------------------------------
                                                                                                                         -
                0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ )



G.B. Beretta                                          Alexandria, 6 June 2008                                      SC076 — Understanding Color
4.2 Uniformity                                                                                                               48
•     The X, Y, Z tristimulus
      coordinates allow us to
      decide if two colors match in
                                              y
      a given context. If there is                      520

      no match, it does not tell us        0.8
                                                                 530

                                                                       540
      how large the perceptual                    510
                                                                             550
                                                                                               Stiles Line Element
      mismatch is                                                                              Ellipses plotted 3 x
                                                                                   560
                                           0.6
•     Consequently, the CIE 1931                  500
                                                                                         570


      chromaticity diagram is not                                                              580


      a perceptually uniform               0.4
                                                                                                     590

                                                                                                           600
      chromaticity space from                                                                                    610
                                                                                                                   620
      which the perception of                        490                                                             630
                                                                                                                       700

      chromaticity can be derived          0.2
      x = X ⁄ (X + Y + Z),                           480

      y = Y ⁄ (X + Y + Z),                               470
      x+y+z = 1
                                                                  0

                                                           460                                                           x
                                                                 45



                                             0                   0.2               0.4               0.6

G.B. Beretta               Alexandria, 6 June 2008                                 SC076 — Understanding Color
4.2.1 Uniform chromaticity diagram                                                                49
                    • The CIE 1976 UCS (Uniform Chromaticity Scale) chromaticity diagram is
                      perceptually uniform
         u' = 4X ⁄ ( X + 15Y + 3Z ) = 4x ⁄ ( – 2x + 12y + 3 )
         v' = 9Y ⁄ ( X + 15Y + 3Z ) = 9y ⁄ ( – 2x + 12y + 3 )
0.6      v'



0.5



0.4



0.3



0.2



0.1

                                                           u'
0.0
   0.0        0.1     0.2   0.3   0.4   0.5   0.6    0.7    0.8


G.B. Beretta                                  Alexandria, 6 June 2008   SC076 — Understanding Color
4.2.2 CIELAB                                                                              50
               1976 CIE L*a*b* color space

•     CIE 1976 lightness, L*
•     A non-linear function to provide a measure that correlates with lightness more
      uniformly
•     Similar lightness distribution to the Munsell Value scale

      L* = 116 ⋅ 3 Y ⁄ Y n – 16
•     Tangential near origin
•     Two color opponent channels a*, b*


      a* = 500 ⋅ { 3 X ⁄ X n – 3 Y ⁄ Y n }
      b* = 200 ⋅ { 3 Y ⁄ Y n – 3 Z ⁄ Z n }
•     Xn, Yn, Zn: reference white
      • D50: 96.422, 100, 82.521; D65: 95.047, 100, 108.883
•     von Kries type adaptation
G.B. Beretta                    Alexandria, 6 June 2008         SC076 — Understanding Color
4.2.3 Color difference formulæ                                                                                     51
•     The CIE has defined two uniform color spaces, 1976 CIE L*u*v* and 1976 CIE
      L*a*b* in which the difference of two color stimuli can be measured
•     u* and v* (but not a* and b*) are coordinates on a uniform chromaticity
      diagram. The third dimension is the psychometric lightness
                     2           2
      C* ab =     a* + b*
      h ab = atan ( b* ⁄ a* )
                                              ΔC* ab 2               ΔH* ab 2
                   ⎛ ----------------⎞ 2 + ⎛ ---------------- ⎞ + ⎛ -----------------⎞
                        ΔL*
      ΔE* 94 =                                              -
                   ⎝k ⋅ S ⎠                ⎝k ⋅ S ⎠               ⎝k ⋅ S ⎠
                         L         L             C         C            H          H

                SL = 1
       S C = 1 + 0.045 ⋅ C* ab
      S H = 1 + 0.015 ⋅ C* ab
      kL = kC = kH = 1
G.B. Beretta                         Alexandria, 6 June 2008                             SC076 — Understanding Color
4.3 Color spaces                                                                                52
               color model operators

•     device dependent spaces
      •        counts received from or sent to a device
      •        typically RGB counts or CMYK percentages

•     device independent spaces
      •        human visual system related
      •        counts for an idealized device

•     colorimetric spaces
      •        analytically derived from the CIE colorimetry system

•     uniform spaces
      •        Euclidean, with a distance metric

•     visually scaled spaces
•     spaces defined by an atlas


G.B. Beretta                        Alexandria, 6 June 2008           SC076 — Understanding Color
4.3.1 Colorimetric spaces                                                               53
   XYZ         + basis for all other CIE color spaces
               – non-uniform
   RGB         + can be produced by additive devices
               + linear transformation of XYZ
               – non-uniform

      R    0.019710 – 0.005494 – 0.002974 X
e.g., G = – 0.009537 0.019363 – 0.000274 Y
      B    0.000638 – 0.001295 0.009816 Z
               matrix elements are the primary colors
   sRGB        + contains non-linearity typical for PC CRTs
               + easy to implement
               – non-uniform and non-linear
   CIELAB      + most uniform CIE space
               + widely used in the printing industry
               – cubic transformation


G.B. Beretta                     Alexandria, 6 June 2008      SC076 — Understanding Color
4.3.1.1 Colorimetric spaces (cont.)                                                54
   CIELUV      + simple transformation of XYZ
               + uniform
               + related to YUV (PAL, SECAM)
               – less uniform than CIELAB
   YIQ         + used for NTSC encoding
               + black and white compatible
               – contains gamma correction
               – non-uniform
   YES, YCC + linear transformations of XYZ
            + black and white compatible
            + opponent color models
            – less uniform than CIELAB and CIELUV
            – YCC contains gamma correction
            – private standards
   L*C*hab     + has perceptual correlates
               + good for gamut mapping
               + perceptually uniform
               – cylindrical
               – not uniform for compression


G.B. Beretta                   Alexandria, 6 June 2008   SC076 — Understanding Color
4.3.2 Uniform color spaces                                                                55
•     Munsell
      •        perceptually uniform
      •        based on atlas

•     CIELAB
      •        colorimetric

•     CIELUV
      •        colorimetric

•     OSA
      •        perceptually uniform
      •        based on atlas

•     Coloroid
      •        æstetically uniform
      •        based on atlas




G.B. Beretta                          Alexandria, 6 June 2008   SC076 — Understanding Color
4.3.3 Visually scaled color spaces                                                        56

•     Munsell
      •        perceptually uniform
      •        based on atlas

•     OSA
      •        perceptually uniform
      •        based on atlas

•     Coloroid
      •        æstetically uniform
      •        based on atlas

•     NCS
      •        atlas with uniform coordinates
      •        not perceptually uniform




G.B. Beretta                          Alexandria, 6 June 2008   SC076 — Understanding Color
4.3.4 Color spaces defined by an atlas                                                      57

•     Munsell
•     OSA
•     Coloroid
•     NCS
      •        Scandinavian, popular in Europe

•     RAL
      •        German, popular in Europe

•     Pantone
      •        popular in the U.S.A.

•     Many atlases defined by government agencies, industrial
      associations, companies



G.B. Beretta                           Alexandria, 6 June 2008   SC076 — Understanding Color
4.4 Uniform discretization errors                                             58
Cartesian coordinates (e.g.,
CIELAB):




                         Cylindrical coordinates (e.g., L*C*hab):




G.B. Beretta             Alexandria, 6 June 2008    SC076 — Understanding Color
5              Color imaging                                                                           59
                                                          Application
                                                             Protocol
                                                                 Format
                                                                        Compression
                                                                           Color image




Requirement for digital color imaging
•     The total size of a page should be such it can be transferred quickly
•     Therefore, the color space must compress well



G.B. Beretta                    Alexandria, 6 June 2008                      SC076 — Understanding Color
5.1 Luma-chroma spaces                                                                           60
                  L        fR ( R )
                  C1 = A ⋅ f ( G )
                            G
                  C2             fB ( B )


                           YIQ                YUV                               YC1C2



                    NTSC         EBU                   SMPTE         CCIR               sRGB
     XYZ            RGB          RGB                    RGB          709




                                                                                Photo
               CIELAB                                          YES               YCC


G.B. Beretta                 Alexandria, 6 June 2008                   SC076 — Understanding Color
5.2 RGB separations                                                                    61

                                                                           R




               G                                                           B




•     Allow quick display — no processing necessary
•     Unsuitable for color image communication — separations not decorrelated


G.B. Beretta                  Alexandria, 6 June 2008        SC076 — Understanding Color
5.3 CIELAB separations                                                  62


                                                          L*




               a*                                         b*




G.B. Beretta        Alexandria, 6 June 2008   SC076 — Understanding Color
5.4 Chroma subsampling                                                       63




                                              L*




                                         b*                a*



G.B. Beretta   Alexandria, 6 June 2008             SC076 — Understanding Color
6              Illumination                                                                               64
•     The spectral power distribution of the light reflected to the eye
      by an object is the product, at each wavelength, of the object's
      spectral reflectance value by the spectral power distribution of
      the light source
                 CWF                      Complexion




           400      500   600   700   400       500       600   700   400     500     600     700

               Incident SPD       x Reflectance curve =                     Reflected SPD

                 Deluxe                   Complexion
                 CWF




           400      500   600   700   400       500       600   700   400     500     600     700


G.B. Beretta                          Alexandria, 6 June 2008                   SC076 — Understanding Color
6.1 Light sources of interest                                                    65

•     At the beginning of color perception there is radiant energy

•     Treatment in color science is slightly different from what we
      learned in high school physics — it can be limited to the visible
      domain

•     The spectral power distribution of a tungsten filament lamp
      depends primarily on the temperature at which the filament is
      operated

•     Typical average daylight has a color temperature of 6504˚K,
      which can be achieved also by Artificial Daylight fluorescent
      lamps, a.k.a. North-light or Color Matching lamps

G.B. Beretta               Alexandria, 6 June 2008     SC076 — Understanding Color
6.2 CIE standard illuminants                                                                                                                            66

•     CIE standard illuminant A                           300

      represents light from a full
      (or blackbody) radiator at                          250




                                                                   relative radiant power
      2854°K
                                                          200                                                 D65
•     CIE standard illuminant D65
                                                                                                              A
      represents a phase of natural
                                                          150
      daylight with a correlated
      color temperature of 6504°K
                                                          100
      CIE standard illuminants B and C were intended to
      represent direct sunlight with a correlated color
      temperature of 4874°K resp. 6774°K. They are         50
      being dropped because they are seriously deficient
      in the UV region (important for fluorescent
      materials)                                                                                                                            wavelength [nm]
                                                            0
                                                             300                            350   400   450   500   550   600   650   700    750    800




G.B. Beretta                             Alexandria, 6 June 2008                                                     SC076 — Understanding Color
6.3 CIE standard sources                                                               67

•     Illuminant refers to a specific spectral radiant power distribution
      incident to the object viewed by the observer
•     Source refers to a physical emitter of radiant power, such as a
      lamp or the sun and sky
•     CIE illuminant A is realized by a gas-filled coiled-tungsten
      filament lamp operating at a correlated color temperature of
      2856°K
•     There are no artificial sources for illuminant D65, due to the
      jagged spectral power distribution. However, some sources
      qualify as daylight simulators for colorimetry
•     For more information see
      http://www.communities.hp.com/online/blogs/mostly_color/archive/2007/06/22/
      HPPost3682.aspx

G.B. Beretta                  Alexandria, 6 June 2008        SC076 — Understanding Color
7              Measuring color                                                           68

•     There are no filters that approximate well the color matching functions
•     There are no artificial sources for the popular illuminants D65 and D50
•     Today’s hardware situation has changed dramatically
      • Embedded processors are inexpensive
      • Holographic gratings are inexpensive
      • Light sources are highly efficient
      • CCD sensors have much less dark noise
•     It is better to perform spectral measurements and let the instrument do the
      colorimetry
•     Spectroradiometer: determine the reflected SPD
•     Spectrophotometer: determine the reflectance curve
      • see drawing on page 64 (Illumination)
•     Because they are a closed system, spectrophotometers are very reliable




G.B. Beretta                   Alexandria, 6 June 2008         SC076 — Understanding Color
7.1 Trusting your instrument                                                               69

Sooner or later all users enter a deep trust crisis in their instruments.
Some survival tips:
•     Illuminate your work area with a source simulating your target illuminant
      • see what the instrument “sees”
•     Compact spectrophotometers have a very small geometry; perpendicularity
      between optical axis and sample, as well as distance to the sample are critical
      • maintain an uncluttered work space
•     The instrument’s light source generates heat, which increases dark current
      noise in the CCD and causes geometric deformations in the grating
      • wait between measurements
      • recalibrate
           • at each session start
           • after each pause
           • after a long series of measurements,
           • when the ambient temperature has changed by more than 5˚C




G.B. Beretta                    Alexandria, 6 June 2008          SC076 — Understanding Color
7.2 Calibration                                                                                                 70
White calibration: adjusts computational parameters so the calculated
tile’s reflectance curve is the same as the absolute reflectance curve
•     do it often

Absolute certification: verifies that the measured color of the tile is
within the tolerance (e.g. 0.6 ΔE units) from the absolute color of the tile
•     important for agreement between laboratories

Relative certification: verifies if the measured color of the tile is within
the tolerance (e.g. 0.3 ΔE units) from the initial color of the tile with the
same instruments
•     important for reproducibility

Collaborative testing: verifies that the entire color measurement
procedure is in agreement with outside laboratories
      Collaborative Testing Services Inc, 21331 Gentry Drive, Sterling, VA 20166, 571-434-1925
      http://www.collaborativetesting.com/


G.B. Beretta                             Alexandria, 6 June 2008                      SC076 — Understanding Color
7.3 Effect of variability                                                              71

•     A measurement is never perfect
•     The effect of variability of color measurement is reduced by using multiple
      measurements
•     How many measurements should I make and average?
•     Rule of thumb: 10× for each variability parameter
      • instrument’s variability: measure each spot — 10×
      • sample uniformity: repeat at several locations — 100×
      • sample variability: repeat for several samples — 1000×
      • …
•     Follow ASTM standard practice E 1345 – 90 to determine how many
      measurements are necessary in each case
      • ASTM, 100 Barr Harbor Drive, West Conshohoken, PA 19428, 610-832-9585,
          http://www.astm.org
•     Improve all process aspects to minimize the required number of measurements
•     ISO 9001




G.B. Beretta                  Alexandria, 6 June 2008        SC076 — Understanding Color
7.4 Geometries of illumination and                                                        72
  viewing
•     On a glossy surface there are mirror-like (specular) reflections
•     There are more reflections in the case of diffuse light sources
•     Since the color of the illuminant is white, specular reflections add white, with
      the effect of desaturating the color
•     Non-metallic glossy surfaces look more saturated in directional than in diffuse
      illumination
•     Matte surfaces scatter the light diffusely — matte surfaces usually look less
      saturated than glossy surfaces
•     Most surfaces are between glossy and matte
•     Diffuse illumination is provided by integrating spheres
      • usually they are provided with gloss traps
•     Instruments with 45/0 and 0/45 geometry are less critical
•     ASTM recommendation for partly glossy samples:
      • use the geometry that minimizes surface effects (usually the one that gives
           lowest Y and highest excitation purity)
•     45/0 geometry gives rise to polarization problems




G.B. Beretta                   Alexandria, 6 June 2008          SC076 — Understanding Color
8              Color reproduction                                              73
In most cases, color reproduction is simple and inexpensive because
of metamerism
Spectral color reproduction: equality of spectral reflectance or SPD
•     rarely needed
•     paint samples, metamerism assessment
Colorimetric reproduction: equality of chromaticities and relative
luminances
•     useful when viewing conditions are the same and light source is
      the same
Exact reproduction: equality of chromaticities, absolute & relative
luminances
•     useful when viewing conditions are identical


G.B. Beretta              Alexandria, 6 June 2008    SC076 — Understanding Color
8.0.1 Reproduction modes (cont.)                                              74

Equivalent reproduction: same appearance of chromaticities,
absolute & relative luminances
•     useful when the luminance level is the same
Corresponding reproduction: same appearance of chromaticities
and relative luminances when the luminance levels are the same
•     current focus of research in color reproduction; CIECAM
Preferred reproduction: achieve more pleasing reproduction of
memory colors by departing from equality of appearance




G.B. Beretta              Alexandria, 6 June 2008   SC076 — Understanding Color
8.1 Additive and substractive color                                                      75
               Mixing of colored lights vs. mixing of colorants

•     Additive color: start with black and add primaries
      •        red green blue

•     Substractive color: start with white and substract complements
      of primaries
      •        cyan magenta yellow




G.B. Beretta                         Alexandria, 6 June 2008   SC076 — Understanding Color
8.1.1 The additive method                                                                  76


•     Probable sensitivity        absorbance
                                                                                S-cone
                                 1.0
      curves of the human eye
                                                                                M-cone
      and the three best lights 0.8
      for additive color                                                        L-cone

      reproduction               0.6                                            Rod
•     Note the strong overlap
                                 0.4
      in the orange-yellow
      interval                   0.2
•     This means that correct
      color reproduction         0.0                                             nm
                                            400 450     500   550     600    650
      cannot be achieved with
      simple trichromatic methods, because there are always unwanted stimulations
•     Hence, the trivial idea of stimulating the cones independently does not work
      with a simple approach



G.B. Beretta                    Alexandria, 6 June 2008          SC076 — Understanding Color
8.1.2 The subtractive method                                                                                  77
•     The additive method has two major disadvantages when the set-up is not
      light-emissive:
      • the required filters significantly reduce the brightness of the image
      • the reproduction of a mosaic can be tricky
•     It is easier to generate colors from a beam of white light and varying the
      proportions of reddish, green, and bluish parts
•     On top to the unwanted stimulations, there is a problem with unwanted
      absorptions, making the subtractive method even harder to master than the
      additive method
                   1.0

                   0.8

                   0.6

                   0.4                      10%
                                            50%
                   0.2
                                            100%
                   0.0
                         400   450          500      550       600   650   700


                   1.2
                   1.0
                   0.8
                   0.6                                                           10%
                   0.4                                                           50%
                   0.2                                                           100%
                   0.0
                         400   450          500      550       600   650   700


                   1.0

                   0.8

                   0.6                                                     10%
                   0.4                                                     50%
                                                                           100%
                   0.2

                   0.0
                         400   450          500      550       600   650   700




G.B. Beretta                         Alexandria, 6 June 2008                        SC076 — Understanding Color
8.1.3 Dithering                                                                               78
•     Color is a usually represented with at least 8 bits per channel,
      for 256 levels
•     Some devices can display less levels
      •        mobile LCD displays often have only 6 bits per channel
      •        most printers have only 1 bit per channel

•     Displays: temporal dithering
•     Printers: spatial dithering, a.k.a. halftoning




G.B. Beretta                        Alexandria, 6 June 2008         SC076 — Understanding Color
8.2 Scan — think — print                                                         79

•     Because of the unwanted stimulations and absorptions, it is
      practically impossible to engineer a color reproduction system
      based on light and lenses producing satisfactory image quality
•     Because of the large amount of data and lengthy computations,
      digital systems are possible only slowly
•     Initially, closed proprietary solutions
•     Later, open solutions based on standards and a color
      management system
      •        SWOP inks
      •        ICC profiles




G.B. Beretta                 Alexandria, 6 June 2008   SC076 — Understanding Color
8.2.1 Managed color reproduction                                                                                              80

                                                                                                                       sRGB
                                                             profile maker

                                                         spectro-                                             business
negative
               YCC
                                                       photometer                                            TIJ printer
                     PhotoCD                                                                                          CMYK
                     scanner
                                                                                                             graphic arts
                                                    CIELAB
                                                                                                              TIJ printer
               RGB
                                                              ICC profile
                                                                                                                       RGB
                graphic arts                           workstation
                  scanner                              and archive                                             digital
positive
                                                                                                            proof printer
               AdobeRGB                             RGB
                                                                                                                      CMYK




                                                                                     Inte
                                                              ICC profile
                     digital




                                                                                      rne
                     camera                               display and                                       platemaker or




                                                                                        t
                                                           softcopy                                          direct press
                               CIELAB+sRGB                                   any
                                      ICC profile                              color rendering dictionary

                                   repository                                      raster image
                                   (database)                                        processor




G.B. Beretta                      Alexandria, 6 June 2008                                    SC076 — Understanding Color
9              Milestones in color printing                                          81
30,000 BCE: hand is commonly used as a stencil by holding it against
a cave wall and blowing powder on it
                          1457: Fust and Schöffer use colored metal plates
                          to print the Psalterium with colored initial
                          letters. They had to discover and solve the
                          problems of color trapping and registration
                          Breakthrough: mass-production of illuminated
                          books
1580–1644: during the Ming dynasty, techniques are perfected for
the mass-production of multicolored book illustrations
               ~1700: invention of the katagami stencil. The stencil’s loose
               elements are connected with silk wires fine enough that ink
               can flow around them, enabling the mass-production of fine
               illustrations. Ukiyo-e — pictures of the floating world

G.B. Beretta                    Alexandria, 6 June 2008    SC076 — Understanding Color
9.0.1 Color printing milestones (cont.)                                        82
1719: Le Blon receives British patent 423 for inventing the
trichromatic printing principle. Yellow, red, blue plus black for
better gray balance and clean blacks
1797: Senefelder invents lithography, enabling the inclusion of a
large number of illustrations in very long run books like the
Encyclopédie
1816: Engelmann invents chromolithography; 6 to 19 partial colors,
sometimes even 24 and 30
1816: Young invents color filters, which will allow to separate color
images
1852: Fox Talbot invents concept of halftone screening
1879: Swan invents line screen
1888: Meisenbach invents crossline screen
G.B. Beretta             Alexandria, 6 June 2008     SC076 — Understanding Color
9.0.2 Color printing milestones (cont.)                                                83
1910: invention of the panchromatic film emulsion, allowing the
use of Maxwell’s filters
From here on all effort goes into color correction (masking)
1937: Neugebauer proposes an eight-color analytical method based
on colorimetry
1948: Hardy and Wurzburg invent the scanner — electronic circuitry
is used to determine the color correction in one single step.
      The 1941 Murray and Morse scanner just tried to simulate masking
      Hardy and Wurzburg’s solved the Neugebauer equations

1957: Patent 2,790,844 — early effort towards gamut mapping
1977: Ichiro Endo receives U.S. patent 4,723,129 for thermal ink jet
technology
1987: Canon launches CLC-1 color copier
G.B. Beretta                  Alexandria, 6 June 2008        SC076 — Understanding Color
10             Color image communication                                       84
                      Application
                         Protocol
                             Format
                                    Compression
                                       Color image




G.B. Beretta               Alexandria, 6 June 2008   SC076 — Understanding Color
10.1 Lossless coding                                                                      85
•     Huffman coding
•     Arithmetic coding
•     LZ coding
•     LZW coding (USP 4,558,302)
•     Flate and deflate (IETF RFC 1951)
•     Binary image compression
      • Group 3 1-d (MH) and 2-d (MR)
           • ITU-T Rec. T.4
      • Group 4 (MMR)
           • ITU-T Rec. T.6
      • JBIG — progressive bi-level image compression
           • ISO 11544 / ITU-T Rec. T.82
           • ITU-T Rec. T.85 — application profile for fax
           • ITU-T Rec. T.43 — bit-plane coding for color fax images using JBIG
      • JBIG2 — lossy/lossless coding for bi-level images
           • ISO 14492 / ITU-T Rec. T.88
           • text halftone, and generic modes
•     lossless JPEG
•     lossless JPEG 2000


G.B. Beretta                   Alexandria, 6 June 2008          SC076 — Understanding Color
10.2 Palette color                                                                   86
               Counting colors

•     24-bit pixels can represent 16 million colors

•     Humans can distinguish 10 million colors

•     A 2×3K image contains
      6 million pixels

•     A 512×512 image contains
      250 thousand pixels

•     A “typical” 5122 image has
      26 thousand colors

•     One byte can represent 256 colors

G.B. Beretta                     Alexandria, 6 June 2008   SC076 — Understanding Color
10.2.1 Color palettes (mapped color)                                                      87
•     Represent original colors by indices into a map with reduced set
      of colors (paint by numbers)
      •        choose N colors (palette)
               • image dependent (adaptive) or image independent (fixed)
               • e.g., median cut
      •        quantize (map) original to palette colors
      •        use look-up table to map index to palette color
      •        may use dither in palettized image




                                                quantize
                        original                             index
                                                     Q




G.B. Beretta                       Alexandria, 6 June 2008      SC076 — Understanding Color
10.3 JPEG                                                                              88
•     No color space specification
•     Baseline JPEG: 4 or less color components
      • Colorimetric color representation is possible
•     Full JPEG: 256 or less color components
      • Discrete spectral color representation is possible
•     Compression can be improved with chroma subsampling



               JPEG 2000

•     Wavelet-based follow-on to JPEG
      • same committee, different contributors
•     Single compression architecture
      • continuous-tone and binary compression
      • lossy, lossless, and lossy-to-lossless coding
      • progressive rendering
•     1–256 color (spectral) components



G.B. Beretta                    Alexandria, 6 June 2008      SC076 — Understanding Color
10.4 Mixed Raster Content — background 89
                T.6                                           T.4
                        black-and-white     black-and-white
                MMR     text and line       text and line      MH
                        diagrams            diagrams



  T.85                  in1
                                   out
                                           in1
                                                       out
                        in2                in2
    JBIG
  black-and-white
  text, halftones,
  stipples, line art,                                                      PSTN
  and so on




                                                                       Multiple, independent
                                                                       compression methods—
               T.42                                           T.43     each optimized for one
                JPEG                                           JBIG    kind of image content
               CIELAB                                         CIELAB


G.B. Beretta                              Alexandria, 6 June 2008             SC076 — Understanding Color
10.4.1 Mixed Raster Content — solution                                                                   90
                           black-and-white
                                               T.44
                           text & digrams
                           as before,          Mixed
                           colored             Raster
                           text               Content
                           too



                                                                         interchange
      black-and-white
      text and line
      diagrams
             black-and-white
             text, halftones,
             stipples, line art,
                    color text     and
     in1 and so graphics
                     on
     in2           out



                                                               MRC is a method for using
                                                               multiple compression methods
                                                               in raster documents that contain
                                                               multiple kinds of content

G.B. Beretta                                 Alexandria, 6 June 2008           SC076 — Understanding Color
10.4.2 Mixed Raster Content — overview                                                        91

•     MRC = Mixed Raster Content
      •        multi-layer model for representing compound images
      •        described in ITU-T Recommendation T.44
               • originally proposed in joint Xerox/HP contribution
      •        efficient processing, interchange and archiving of raster-oriented pages
               with a mixture of multilevel and bilevel images

•     Technical approach
      •        segmentation of an image into multiple layers (planes), by image content
      •        use spatial resolution, color representation and compression method
               matched to the content of each layer

•     Compound image architecture
      •        framework for using compression methods

•     Performance
      •        can achieve compression ratios of several 100 to 1 on typical documents



G.B. Beretta                        Alexandria, 6 June 2008         SC076 — Understanding Color
10.4.3 Mixed Raster Content — model                                                                  92
                      Image

                                                                 3-layer model
    black-and-white
    text & digrams
    colored text                                         •   Foreground
                                                             •   multilevel, e.g., text color
                                    bla                      •   JBIG @ 12 bpp, 100 dpi
                                        ck
                                  red
                                                         •   Mask
                                                             •   bilevel, e.g., text shape
                                 bla
                               tex ck-a
                                                             •   MMR @ 1 bpp, 400 dpi
                                  t     n
                              co & dig d-wh
                                lor         i
                                    ed rams te
                                        tex
                                                         •   Background
                                             t               •   multilevel, e.g., contone im.
                                                             •   JPEG @ 24 bpp, 200 dpi


                                                         Image = M • FG + M’ • BG


G.B. Beretta                       Alexandria, 6 June 2008                 SC076 — Understanding Color
10.4.4 Internet fax                                                                         93
               What is it?

•     Store-and-forward Internet fax
      •        scanned document transmission using e-mail attachments
      •        ITU-T standards and IETF protocols
      •        uses ESMTP with delivery confirmation and capabilities exchange

•     ITU-T Recommendation T.37 — approved September 1999
      •        references IETF standards
               • requires use of TIFF-FX
      •        Simple Mode — TIFF-FX Profile S: April 1999
      •        minimal b&w with no delivery confirmation or capability exchange
      •        Full Mode — TIFF-FX all profiles: September 1999
               • range of b&w and color with delivery confirmation and capability
                  exchange




G.B. Beretta                       Alexandria, 6 June 2008        SC076 — Understanding Color
10.4.4.1 Internet fax — configurations                                           94




                 Internet
                                                                all-in-one

   workstation




                                               PSTN




                             on/off ramp                          fax


G.B. Beretta         Alexandria, 6 June 2008          SC076 — Understanding Color
10.4.5 IPP — Internet Printing Protocol                                                 95
               What is it?


•     Firewall problem
•     IETF standard developed with help from the Printer Working
      Group
•     Client-server protocol for distributed printing on the Internet
      •        intended to replace LPR/LPD

•     Uses HTTP 1.1 POST application protocol
      •        Internet media type: application/ipp




G.B. Beretta                        Alexandria, 6 June 2008   SC076 — Understanding Color
10.4.5.1 IPP — Internet Printing Protocol                                                       96
               Sample configurations

   Client to printer


                                                            IPP




                           client                                          IPP object

    Client to server


                                              IPP




                           client                        IPP object


G.B. Beretta                   Alexandria, 6 June 2008                SC076 — Understanding Color
10.5 Document ecosystems                                                                                  97
               Seamless office imaging

•     Scanners, copiers, connected to Ethernet instead of computer
•     Documents distributed via e-mail, fax servers, remote printers,
      or ISV applications


                               HP 9100C                                              Imaging
                                Service                                             Application
                                                    write                read
                      TCP/IP


                                                            image +
                                                            metadata


                                                            NOTIFY.DAT



  HP 9100C                 Windows                         Shared                    Application
Digital Sender              Server                          Disk                       Server
G.B. Beretta                     Alexandria, 6 June 2008                        SC076 — Understanding Color
11             Color appearance modeling                                                     98

•     Recommended model: CIECAM02
•     Do not use an appearance model when
      •        stimulus specification is simple (CIELAB, sRGB, …)
      •        simple color tolerances (CIE94)
      •        only one viewing condition
      •        it is not clear it will help

•     What they allow you to do
      •        map from measurements to color names
      •        predict color matches across viewing conditions
               • render color across media
      •        gain a deeper understanding of color
      •        no metric for color differences




G.B. Beretta                        Alexandria, 6 June 2008        SC076 — Understanding Color
11.1 Cognitive context                                                                                                99

    stimulus            detectors   early mechanisms              pictorial register

                                                           color
                                                           edges
                                                          contour
                                                          motion
                                                           depth
                                                            …
                                                                              context parameters

                                                                                                        chroma
                                                                                                 etc.
                                                                                                                hue
                                    Color lexicon                                                   lightness

                                                                            chroma               internal
                                                                     etc.
                                                                                                color space
                                      amber                                         hue
                                                                        lightness

               action               color name                     apparent color
                                                                   representation




G.B. Beretta                            Alexandria, 6 June 2008                           SC076 — Understanding Color
11.2 CIECAM02                                                                              100
•     Conditions modeled
      •        adaptation
      •        discounting the illuminant
      •        surround effects

•     Predictions missing from the model
      •        rod contributions
      •        color difference metric
      •        constant hue lines
      •        Helson-Judd effect
      •        Helmholtz-Kohlrausch effect

•     Graphical representation
      •        CIECAM02 is represented in cylindrical coordinates
               • lightness J
               • chroma C
               • hue h
      •        trigonometric transformation necessary for plots

•     Includes the 5 years of revisions since CIECAM97s

G.B. Beretta                        Alexandria, 6 June 2008         SC076 — Understanding Color
11.3 The color selection problem                                                     101

                                                   Surround
               10º
                      Background
                                            Color
                                          considered

                     2º

      Adapting
        field                      Proximal field


•     This user interface problem cannot be solved without color appearance model
•     Currently users converge towards their intended rendering by trial and error


G.B. Beretta                  Alexandria, 6 June 2008         SC076 — Understanding Color
11.4 The gamut mapping problem                                                                      102
                                                                       b*
                                                                                    Printer




                                                                                                      a*




      Measure original                               Monitor


               Compute appearance

                                                                        CG Image
                         Gamut compression


                              Modify appearance (L*C*hab)


                                        Compute colorant quantities



G.B. Beretta                                 Alexandria, 6 June 2008         SC076 — Understanding Color
Understanding Color
Understanding Color
Understanding Color
Understanding Color
Understanding Color

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Understanding Color

  • 1. Understanding Color Giordano Beretta Hewlett-Packard Laboratories http://www.inventoland.net/imaging/uc/ Alexandria 2008
  • 2. Course objectives 1 • Develop a systematic understanding of the principles of color perception and encoding • Understand the differences between the various methods for color imaging and communication • Gain a more realistic expectation from color reproduction • Develop an intuition for • trade-offs in color reproduction systems • interpreting the result of a color measurement G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 3. What is color? 2 • Color is an illusion • Colorimetry: the art to predict an illusion from a physical measurement • Experience is much more important than knowing facts or theories • The physiology of color vision is understood only to a very small degree • Physiology: physical stimulus → physiological response • Psychophysics: physical stimulus → behavioral response What is essential is invisible to the eye Antoine de Saint-Exupéry (The Little Prince) G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 4. 1 Terminology 3 CIE definition 845-02-18: (perceived) color Attribute of a visual perception consisting of any combination of chromatic and achromatic content. This attribute can be described by chromatic color names such as yellow, orange, brown, red, pink, green, blue, purple, etc., or by achromatic color names such as white, gray, black, etc., and qualified by bright, dim, light, dark etc., or by combinations of such names Perceived color depends on the spectral distribution of the color stimulus, on the size, shape, structure and surround of the stimulus area, on the state of adaptation of the observer’s visual system, and on the observer’s experience of the prevailing and similar situations of observation Perceived color may appear in several modes of appearance. The names for various modes of appearance are intended to distinguish among qualitative and geometric differences of color perceptions G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 5. 1.0.1 Color term categories 4 Subjective color term: A word used to describe a color attribute perceived by a human. Example: the colorfulness of a flower Objective color term: A word used to describe a physical quantity related to color that can be measured. Example: the energy radiated by a source We use objective color terms as correlates to subjective color terms G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 6. 1.0.2 Subjective color terms — Hue 5 Hue: The attribute of a color perception denoted by blue, green, yellow, red, purple, and so on hue scale Unique hue: A hue that cannot be further described by use of the hue names other than its own. There are four unique hues, each of which shows no perceptual similarity to any of the others: red, green, yellow, and blue G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 7. 1.0.3 Brightness 6 Brightness: The attribute of a visual sensation according to which a given visual stimulus appears to be more or less intense, or according to which the visual stimulus appears to emit more or less light Objective term: luminance (L) brightness scale G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 8. 1.0.4 Lightness 7 Lightness: The attribute of a visual sensation according to which the area in which the visual stimulus is presented appears to emit more or less light in proportion to that emitted by a similarly illuminated area perceived as a “white” stimulus Objective terms: luminance factor (β), CIE lightness (L*) • Brightness is absolute, lightness is relative to an area perceived as white G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 9. 1.0.5 Colorfulness 8 Chromaticness or Colorfulness: The attribute of a visual sensation according to which an area appears to exhibit more or less of its hue. In short: the extent to which a hue is apparent Objective term: CIECAM02 M G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 10. 1.0.5.1 Colorfulness — Chroma 9 Chroma: The attribute of a visual sensation which permits a judgement to be made of the degree to which a chromatic stimulus differs from an achromatic stimulus of the same brightness In other words, chroma is an attribute orthogonal to brightness: absolute colorfulness; we perceive a color correctly independently of the illumination level Objective term: CIE chroma (C*uv, C*ab) G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 11. 1.0.5.2 Colorfulness — Saturation 10 Saturation: The attribute of a visual sensation which permits a judgement to be made of the degree to which a chromatic stimulus differs from an achromatic stimulus regardless of their brightness In other words, it is the colorfulness of an area judged in proportion to its brightness: relative colorfulness; we can judge the uniformity of an object’s color in the presence of shadows and independently of the incident light’s angle Objective terms: purity (p), CIE saturation (Suv) saturation scale Colorfulness is absolute, chroma is relative to a white area and absolute w.r.t. brightness, saturation is in proportion to brightness G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 12. 1.1 Our goal 11 • We would like to be able to predict the color of a sample by making a measurement • Humans can distinguish about 7 to 10 million different colors — just name them and build an instrument that identifies them • Task: find good correlates to the subjective color terms • Some observations: • If you want to buy a skirt or a pair of slacks to match a jacket, you cannot match the color by memory — you have to take the jacket with you • Just matching in the store light is insufficient, you have to match also under the incandescent light in the dressing room and outdoors • You always get the opinion of your companion or the store clerk • Three fundamental components of measuring color: • light sources • samples illuminated by them • observers G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 13. 1.2 Spectral curves 12 quantities we can measure • The spectral power curve gives at each wavelength the power (in watts), i.e., the rate at which energy is received from the light source • The spectral reflectance curve gives at each wavelength the percentage of incident light that is reflected 0.40 reflectance human complexion 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 400 450 500 550 600 650 700 nm G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 14. 1.2.1 Spectral color reproduction 13 • By spectral color reproduction we intend the physically correct reproduction of color, i.e., the duplication of the original object's spectrum • The general reproduction methods (micro-dispersion and Lippmann) are too impractical for normal use • For some special applications like painting restoration or illuminant reconstruction, the spectrum may be sampled at a small number of intervals and combined with principal component analysis • Fortunately, spectral color reproduction is required only in rare cases, such as paint swatches in catalogs, and in this cases it is often possible to use identical dyes Our aim is to achieve a close effect for a normal viewer under average viewing conditions Mathematically: build a simple model of color vision G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 15. 2 Color theories 14 • 800 B.C.E. — Indian Upanishads • there are relations among colors • 400 B.C.E. — Hellenic philosophers • Plato: light or fire rays emanate from the eyes • Epicurus: replicas of objects enter the eyes • First Millennium — Arab school, pure science • Abu Ali Mohammed Ibn al Hazen: image is formed within the eye like in a camera obscura • 15th century — Renaissance, technology • Leonardo da Vinci: • color perception • color order system • black & white are colors • 3 pairs of opponent colors (black–white, red–green, yellow–blue) • simultaneous contrast • used color filters to determine color mixtures G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 16. 2.0.1 Opponent colors 15 W Y R G W B Y Y K G R B W K G R B Note: rendered with chiaro-scuro technique G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 17. 2.0.2 Color theories (cont.) 16 • 18th century — Enlightenment, physics & chemistry • Isaac Newton: • spectral dispersion, white can be dispersed in a spectrum by a prism • colors of objects relate to their spectral reflectance • light is not colored and color perception is elicited in the human visual system • 19th century — scientific discovery • Thomas Young: trichromatic theory • Hermann von Helmholtz: spectral sensitivity curves • Ewald Hering: • opponent color theory (can explain hues, saturation, and why there is no reddish green or yellowish blue) • black and dark gray are not produced by the absence of light but by a lighter surround G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 18. 2.0.3 Color theories (cont.) 17 • 20th century — advanced scientific instruments • Johannes A. von Kries: chromatic adaptation • why is white balance necessary? • Georg Elias Müller & Erwin Schrödinger: zone theory • physiological evidence for inhibitory mechanisms becomes available in the 1950s • molecular biology • functional MRI techniques • see http://webvision.med.utah.edu/ for the latest progress G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 19. 2.1 Color vision is not based on a bitmap 18 • Vision is based on contrast • Vision is not hierarchical. The simple model distal event ↓ proximal stimulus ↓ brain event is very questionable. It is believed that feedback loops exist between all 26 known areas of visual processing • In fact, it has been proved that a necessary condition of some activity in even the primary visual cortex is input from “higher” areas • Like the other sensory systems, vision is narcissistic • Many sensory signals are non-correlational — a given signal does not always indicate the same property or event in the world The “inner eye’s” function is not to understand what the sensory states indicate Science 17 March 2006: Vol. 311. no. 5767, pp. 1606 - 1609 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 20. 2.1.1 Cognitive model for color appearance 19 stimulus detectors early mechanisms pictorial register color edges contour motion depth … context parameters chroma etc. hue Color lexicon lightness chroma internal etc. color space amber hue lightness action color name apparent color representation • Reliable color discrimination: 1 week • Color-opponent channels: 3 months • Color constancy: 4 months • Internal color space • Color names G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 21. 2.1.2 Memory colors 20 • Vision is not hierarchical • Delk & Fillenbaum experiment (1965) • We tend to see colors of familiar objects as we expect them to be Surround 10º Sky Complexion 2º Adapting field Vegetation G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 22. 2.2 Color vision physiology 21 • The retina has a layer of photoreceptors, which grow like hair (10μm per day). They are of two kinds: rods and cones • The cones are of three kinds, depending on the pigments they contain. One pigment absorbs reddish light, one absorbs greenish light, and one absorbs bluish light • This leads to the method of trichromatic color reproduction, in which we try to stimulate independently the three kinds of cones ls s cel um ib er lio n ls ll s ells th eli rvef ng cel ce lls ta l ce ne c ones t epi ne ga ne on & co s & c men tic in al a cri o lar riz d op ret am bip ho ro rod pig stimulus G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 23. 2.2.1 Photoreceptors 22 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color Credit: Carlos Rozas (CanalWeb, Chile) http://webvision.med.utah.edu/movies/3Drod.mov
  • 24. 2.2.1.1 Outer segment 23 http://webvision.med.utah.edu/movies/discs.mov http://webvision.med.utah.edu/movies/phago4.mov Credit: Helga Kolb G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 25. 2.2.2 Evolution 24 • From the difference in the amino-acid sequences for the various photoreceptor genes it is clear that the human visual system did not evolve according to a single design Finding Rod and S Mechanisms L and M Mechanisms Distribution perifoveal foveal Anatomy one class two classes Bipolar circuitry (only on) (on and off) Spatial resolution low high Temporal resolution low high Psychophysics Weber fraction high low Wavelength sensitivity short medium Response function saturates does not saturate Latencies long short ERG-off-effect negative positive Electrophysiology Ganglion cell response afterpotential no afterpotential Receptive field large small Vulnerability high low Genetics autosomal sex-linked Source: Eberhart Zrenner, 1983 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 26. 2.2.3 Catching photons 25 • Retinal pigments: rhodopsin, cyanolabe, chlorolabe, erythrolabe • lysine attaches chromophore to a protein backbone • electronic excitation (two-photon catch) initiates a large shift in electron density in less than 10–15 seconds • shift activates rotation around two double-bonded carbon atoms in the backbone • entire photocycle lasts less than a picosecond (10–12 sec.) • photoisomerization induces shift in positive charge perpendicular to membrane sheets containing the protein • this generates a photoelectric signal with a less than 5 psec. rise time • forward reaction is completed in ~50 μsec. (10–6 sec.) • Quantum efficiency: measure of the probability S harpe et al. ∑Human R ed, G reen, and R ed-G reen Hybrid C one P igments that the reaction will take place after the absorption of a photon of light • 4 pigments sensitized to photons at 4 energy levels (wavelength): L, M, S, and rods G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 27. 2.2.4 Phototransduction 26 Credit: Helga Kolb,http://webvision.med.utah.edu/movies/trasduc.mov G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 28. 2.2.5 Catch probabilities 27 • Quantum energy of a photon: hν • For each pigment, there is a probability distribution for a reaction, depending on the photon’s wavelength • w(λ) dλ • What counts is not the energy of a single photon, but the average • For a spectral power distribution Pλ: S = ∫ Pλ w(λ) dλ absorbance S-cone 1.0 M-cone 0.8 L-cone 0.6 Rod 0.4 0.2 nm 0.0 400 450 500 550 600 650 Dartnall, H. J. A., Bowmaker, J. K., & Mollon, J. D. (1983). Human visual pigments: microspectrophotometric results from the eyes of seven persons. Proceedings of the Royal Society of London, B 220, 115-130 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 29. 2.2.6 Retinal mechanisms 28 Surround Center Surround Retinal Amacrine Bipolar Horizontal Receptor ganglion cell cell cell cell • Receptors in retina are not like pixels in a CCD • Receptive field: area of visual field that activates a retinal ganglion (H.K. Hartline, 1938) • Center-surround fields allow for adaptive coding (transmit contrast instead of absolute values) • Horizontal cells presumed to inhibit either its bipolar cell or the receptors: opponent response in red–green and yellow–blue potentials (G. Svaetichin, 1956) • Balance of red–green channel might be determined by yellow • Retinal ganglion can be tonic or phasic: pathway may also be organized by information density or bandwidth G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 30. 2.2.7 Parvocellular and magnocellular pathways 29 P– M– Originating retinal ganglion cells Tonic Phasic Fast (mostly transient responses, some sustained, Temporal resolution Slow (sustained responses, low conduction velocity) high conduction velocity) Chromatic Luminance Modulation dominance Adaptation occurs at high frequencies Adaptation occurs at all frequencies Receives mostly combined (broadband) input Receives mostly opponent type input from cones Color from M and L cones, both from the center and sensitive to short and long wavelengths from the surround of receptive fields Contrast sensitivity Low (threshold > 10%) High (threshold < 2%) LGN cell saturation Linear up to about 64% contrast At 10% Spatial resolution High (small cells) Low (large cells) When fixation is strictly foveal, extraction of high spatial frequency information (test gratings), Responds to flicker Spatio-temporal resolution reflecting small color receptive fields Short integration time Long integration time Could be a site for both a lightness channel as for Might be a site for achromatic channels because opponent-color channels. The role depends on the the spectral sensitivity is similar to Vλ, it is more Relation to channels spatio-temporal content of the target used in the sensitive to flicker, and has only a weak opponent experiment color component Possible main role in the visual Sustain the perception of color, texture, shape, and Sustain the detection of movement, depth, and system fine stereopsis flicker G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 31. 2.2.8 Color constancy 30 Optic tract Lateral Primary Blob geniculate visual Optic cortex body radiations • Axons of retinal ganglion cells in optical nerve terminate at LGN and synapse with neurons radiating to striate cortex • LGN might generate masking effects; combination with saccadic motion of eye • Blobs in area 17 consist mainly of double opponent cells • May be site for color constancy • Requires input from V4 (Zeki) Why is white balancing necessary in color reproduction? G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 32. 2.3 Limited knowledge 31 • Reaction time at rhodopsin level: femtoseconds • Reaction time at perceptual level: seconds • From photon catches to constant color names We do not know exactly what happens in-between • Examples: simultaneous contrast, chromatic induction G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 33. 2.3.1 1 color appears as 2 32 Appearance mode Three flat objects or picture of a white cube illuminated from the top and right? G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 34. 2.4 Basis for colorimetry 33 • Too many unknowns in physiology and cognitive processes • Cannot yet build accurate color vision model • Unlike auditory system, visual system is not spectral but integrative • Advantage of integrative system: metamerism • Basis of colorimetry: 1. Instead of a physiological model, build a psychophysical model • Physiology: physical stimulus → physiological response • Psychophysics: physical stimulus → behavioral response 2. Assume additivity 3. Keep the viewing conditions constant G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 35. 3 Colorimetry 34 Colorimetry is the branch of color science concerned with specifying numerically the color of a physically defined visual stimulus in such a manner that: 1. when viewed by an observer with normal color vision, under the same observing conditions, stimuli with the same specification look alike, 2. stimuli that look alike have the same specification, and 3. the numbers comprising the specification are functions of the physical parameters defining the spectral radiant power distribution of the stimulus Trichromatic generalization: over a wide range of conditions of observation, many color stimuli can be matched in color completely by additive mixtures of three fixed primary stimuli whose radiant powers have been suitably adjusted (proportionality). In addition, the color stimuli combine linearly, symmetrically, and transitively Grassmann’s laws of additive color mixture G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 36. 3.1 Color matching 35 Colors are assessed by matching them with reference colors on a small-field bipartite screen: G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 37. 3.1.1 Color-matching functions 36 Given a monochromatic stimulus Qλ of wavelength λ, it can be written as Qλ = RλR + GλG + BλB, where Rλ, Gλ, and Bλ are the spectral tristimulus values of Qλ Assume an equal-energy stimulus E whose mono-chromatic constituents are Eλ (equal-energy means Eλ ≡ 1) The equation for a color match involving a mono-chromatic constituent Eλ of E is Eλ = r(λ)R + g(λ)G + b(λ)B, where r(λ), g(λ), and b(λ), are the spectral tristimulus values of Eλ. The sets of such values are called color-matching functions 3.0 Stiles-Burch (1955;1959) 2.5 2.0 b(λ) 1.5 g(λ) 1.0 r(λ) 0.5 0.0 nm -0.5 400 500 600 700 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 38. 3.1.2 Metameric stimuli 37 Consider two color stimuli Q1 = R1R + G1G + B1B Q2 = R2R + G2G + B2B 0.6 reflectance If Q1 and Q2 have different spectral radiant 0.5 D power distributions, but C R1 = R2 and G1 = G2 and B1 0.4 B = B2, the two stimuli are A called metameric stimuli 0.3 • Color reproduction 0.2 works because of metamerism 0.1 nm 0.0 400 500 600 700 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 39. 3.1.2.1 Kinds of metamerism 38 • Illuminant metamerism • example: daylight and a D65 simulation fluorescent lamp • Object metamerism • example: metameric inks (see metamerism kit) • Sensor metamerism • example: scanner and human visual system • Complex metamerism • example: two inks metameric under two illuminants G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 40. 3.2 Chromaticity diagrams 39 We can normalize the color-matching functions and thus obtain new quantities r (λ) = r (λ) / [r (λ) + g(λ) + b(λ)] g(λ) = g(λ) / [r (λ) + g(λ) + b(λ)] b(λ) = b(λ) / [r (λ) + g(λ) + b(λ)] with r(λ) + g(λ) + b(λ) = 1 2.0 g(m) The locus of chromaticity points 1.5 for monochromatic colors so determined is called the spectrum 1.0 2° pilot group Stiles-Burch (1955) locus in the (r, g)-chromaticity diagram 0.5 r(m) 0.0 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 -0.5 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 41. 3.2.1 Imaginary color stimuli 40 • The fact that the color-matching functions and the chromaticity coordinates can be negative presents a problem when the tristimulus values are computed from a spectral radiant power distribution • Because the color- matching space is spectrum locus linear, a linear transformation can 2.0 be applied to the primary stimuli to A: ~2856˚K obtain new 1.5 Planckian locus imaginary stimuli D65: ~6504˚K that lie outside the ∞ chromaticity region 1.0 bounded by the spectrum locus. This ensures that the 0.5 z2(λ) chromaticity y2(λ) coordinates are x2(λ) never negative nm 0.0 400 500 600 700 800 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 42. 3.3 CIE 1931 standard colorimetric 41 observer We want to obtain results valid for the group of normal trichromats (95% of population) Because R = ∫ P λ r (λ ) d λ , G = ∫ Pλ g(λ) dλ, B = ∫ Pλ b(λ) dλ, an ideal observer can be defined by specifying values for the color- matching functions The Commission Internationale de l'Éclairage (CIE) has recommended such tables containing x(λ), y(λ), z(λ) for λ ∈ [360 nm, 830 nm] in 1 nm steps G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 43. 3.3.0.1 CIE 1931 Observer (cont.) 42 In addition to the color-matching properties, the CIE 1931 Standard Observer is such that it has also the heterochromatic brightness- matching properties. The latter is achieved by choosing y (λ) to coincide with the photopic luminous efficiency function X and Z are on the alychne, which in the chromaticity diagram is a straight line on which are located the chromaticity points of all stimuli having zero luminance The data is based averaging the results a) on color matching in a 2° field of 17 observers and b) the relative luminances of the colors of the spectrum, averaged for about 100 observers G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 44. 3.4 Tristimulus normalization 43 • X, Y, and Z are defined up to a common normalization factor. This factor is different for objects and for emissive sources • The perfect reflecting diffuser is an ideal isotropic diffuser with a reflectance equal to unity • The perfect reflecting diffuser is completely matt and is entirely free from any gloss or sheen. The reflectance is equal to unity at all wavelengths • When the tristimulus values are measured with an instrument, YL represents a photometric measure, such as luminance. For object surfaces it is customary to scale X, Y, Z, so that Y = 100 for the perfect diffuser In practice a working standard such as a BaSO4 plate or a ceramic tile is used in lieu of the perfect diffuser • For emissive sources there is no illuminant and therefore the perfect diffuser is not relevant. So it is customary to use the photometric measures G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 45. 4 Objective color terms 44 quantities we can measure Dominant wavelength: Wavelength of the monochromatic stimulus that, when additively mixed in suitable proportions with a specified achromatic stimulus, matches the color stimulus considered (In disuse, replaced by chromaticity) Luminance: The luminous intensity in a given direction per unit projected area L v = K m ∫ L e, λ V ( λ ) dλ λ where Km is the maximum photopic luminous efficacy (683 lm W–1), Le,λ the radiance, and V(λ) the photopic efficiency Luminance factor: The ratio of the luminance of a color to that of a perfectly reflecting or transmitting diffuser identically illuminated Symbol: β G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 46. 4.1 Y 45 Y stimulus (luminosity in some literature): In the XYZ system the luminance depends entirely on the Y stimulus. The Y values of any two colors are proportional to their luminances. Therefore, Y gives the percentage reflection or transmission directly, where a perfectly reflecting diffuser or transmitting color has a value of Y = 100 Y = V where V is the luminance of the stimulus computed in accordance with the luminous efficiency function V(λ) Application: conversion of a color image to black and white G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 47. 4.1.1 Excitation purity 46 Excitation purity: A measure of the proportions of the amounts of the monochromatic stimulus and of the specified achromatic stimulus that, when additively mixed, match the color stimulus considered (In disuse, replaced by chromaticity) x – xw y – yw p c = ------------------ or p c = ------------------ xb – xw yb – yw where w denotes the achromatic stimulus and b the boundary color stimulus G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 48. 4.1.2 Chromaticity 47 Chromaticity: Proportions of the amounts of three color-matching stimuli needed to match a color (see p. 39). Relationship between chromaticity coordinates r(λ), g(λ), b(λ) and x(λ), y(λ), z(λ) of a given spectral stimulus of wavelength λ are expressed by the projective transformation 0.49000r ( λ ) + 0.31000g ( λ ) + 0.20000b ( λ ) x ( λ ) = ---------------------------------------------------------------------------------------------------------- - 0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ ) 0.17697r ( λ ) + 0.81240g ( λ ) + 0.01063b ( λ ) y ( λ ) = ---------------------------------------------------------------------------------------------------------- - 0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ ) 0.00000r ( λ ) + 0.01000g ( λ ) + 0.99000b ( λ ) z ( λ ) = ---------------------------------------------------------------------------------------------------------- - 0.66697r ( λ ) + 1.13240g ( λ ) + 1.20063b ( λ ) G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 49. 4.2 Uniformity 48 • The X, Y, Z tristimulus coordinates allow us to decide if two colors match in y a given context. If there is 520 no match, it does not tell us 0.8 530 540 how large the perceptual 510 550 Stiles Line Element mismatch is Ellipses plotted 3 x 560 0.6 • Consequently, the CIE 1931 500 570 chromaticity diagram is not 580 a perceptually uniform 0.4 590 600 chromaticity space from 610 620 which the perception of 490 630 700 chromaticity can be derived 0.2 x = X ⁄ (X + Y + Z), 480 y = Y ⁄ (X + Y + Z), 470 x+y+z = 1 0 460 x 45 0 0.2 0.4 0.6 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 50. 4.2.1 Uniform chromaticity diagram 49 • The CIE 1976 UCS (Uniform Chromaticity Scale) chromaticity diagram is perceptually uniform u' = 4X ⁄ ( X + 15Y + 3Z ) = 4x ⁄ ( – 2x + 12y + 3 ) v' = 9Y ⁄ ( X + 15Y + 3Z ) = 9y ⁄ ( – 2x + 12y + 3 ) 0.6 v' 0.5 0.4 0.3 0.2 0.1 u' 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 51. 4.2.2 CIELAB 50 1976 CIE L*a*b* color space • CIE 1976 lightness, L* • A non-linear function to provide a measure that correlates with lightness more uniformly • Similar lightness distribution to the Munsell Value scale L* = 116 ⋅ 3 Y ⁄ Y n – 16 • Tangential near origin • Two color opponent channels a*, b* a* = 500 ⋅ { 3 X ⁄ X n – 3 Y ⁄ Y n } b* = 200 ⋅ { 3 Y ⁄ Y n – 3 Z ⁄ Z n } • Xn, Yn, Zn: reference white • D50: 96.422, 100, 82.521; D65: 95.047, 100, 108.883 • von Kries type adaptation G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 52. 4.2.3 Color difference formulæ 51 • The CIE has defined two uniform color spaces, 1976 CIE L*u*v* and 1976 CIE L*a*b* in which the difference of two color stimuli can be measured • u* and v* (but not a* and b*) are coordinates on a uniform chromaticity diagram. The third dimension is the psychometric lightness 2 2 C* ab = a* + b* h ab = atan ( b* ⁄ a* ) ΔC* ab 2 ΔH* ab 2 ⎛ ----------------⎞ 2 + ⎛ ---------------- ⎞ + ⎛ -----------------⎞ ΔL* ΔE* 94 = - ⎝k ⋅ S ⎠ ⎝k ⋅ S ⎠ ⎝k ⋅ S ⎠ L L C C H H SL = 1 S C = 1 + 0.045 ⋅ C* ab S H = 1 + 0.015 ⋅ C* ab kL = kC = kH = 1 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 53. 4.3 Color spaces 52 color model operators • device dependent spaces • counts received from or sent to a device • typically RGB counts or CMYK percentages • device independent spaces • human visual system related • counts for an idealized device • colorimetric spaces • analytically derived from the CIE colorimetry system • uniform spaces • Euclidean, with a distance metric • visually scaled spaces • spaces defined by an atlas G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 54. 4.3.1 Colorimetric spaces 53 XYZ + basis for all other CIE color spaces – non-uniform RGB + can be produced by additive devices + linear transformation of XYZ – non-uniform R 0.019710 – 0.005494 – 0.002974 X e.g., G = – 0.009537 0.019363 – 0.000274 Y B 0.000638 – 0.001295 0.009816 Z matrix elements are the primary colors sRGB + contains non-linearity typical for PC CRTs + easy to implement – non-uniform and non-linear CIELAB + most uniform CIE space + widely used in the printing industry – cubic transformation G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 55. 4.3.1.1 Colorimetric spaces (cont.) 54 CIELUV + simple transformation of XYZ + uniform + related to YUV (PAL, SECAM) – less uniform than CIELAB YIQ + used for NTSC encoding + black and white compatible – contains gamma correction – non-uniform YES, YCC + linear transformations of XYZ + black and white compatible + opponent color models – less uniform than CIELAB and CIELUV – YCC contains gamma correction – private standards L*C*hab + has perceptual correlates + good for gamut mapping + perceptually uniform – cylindrical – not uniform for compression G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 56. 4.3.2 Uniform color spaces 55 • Munsell • perceptually uniform • based on atlas • CIELAB • colorimetric • CIELUV • colorimetric • OSA • perceptually uniform • based on atlas • Coloroid • æstetically uniform • based on atlas G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 57. 4.3.3 Visually scaled color spaces 56 • Munsell • perceptually uniform • based on atlas • OSA • perceptually uniform • based on atlas • Coloroid • æstetically uniform • based on atlas • NCS • atlas with uniform coordinates • not perceptually uniform G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 58. 4.3.4 Color spaces defined by an atlas 57 • Munsell • OSA • Coloroid • NCS • Scandinavian, popular in Europe • RAL • German, popular in Europe • Pantone • popular in the U.S.A. • Many atlases defined by government agencies, industrial associations, companies G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 59. 4.4 Uniform discretization errors 58 Cartesian coordinates (e.g., CIELAB): Cylindrical coordinates (e.g., L*C*hab): G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 60. 5 Color imaging 59 Application Protocol Format Compression Color image Requirement for digital color imaging • The total size of a page should be such it can be transferred quickly • Therefore, the color space must compress well G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 61. 5.1 Luma-chroma spaces 60 L fR ( R ) C1 = A ⋅ f ( G ) G C2 fB ( B ) YIQ YUV YC1C2 NTSC EBU SMPTE CCIR sRGB XYZ RGB RGB RGB 709 Photo CIELAB YES YCC G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 62. 5.2 RGB separations 61 R G B • Allow quick display — no processing necessary • Unsuitable for color image communication — separations not decorrelated G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 63. 5.3 CIELAB separations 62 L* a* b* G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 64. 5.4 Chroma subsampling 63 L* b* a* G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 65. 6 Illumination 64 • The spectral power distribution of the light reflected to the eye by an object is the product, at each wavelength, of the object's spectral reflectance value by the spectral power distribution of the light source CWF Complexion 400 500 600 700 400 500 600 700 400 500 600 700 Incident SPD x Reflectance curve = Reflected SPD Deluxe Complexion CWF 400 500 600 700 400 500 600 700 400 500 600 700 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 66. 6.1 Light sources of interest 65 • At the beginning of color perception there is radiant energy • Treatment in color science is slightly different from what we learned in high school physics — it can be limited to the visible domain • The spectral power distribution of a tungsten filament lamp depends primarily on the temperature at which the filament is operated • Typical average daylight has a color temperature of 6504˚K, which can be achieved also by Artificial Daylight fluorescent lamps, a.k.a. North-light or Color Matching lamps G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 67. 6.2 CIE standard illuminants 66 • CIE standard illuminant A 300 represents light from a full (or blackbody) radiator at 250 relative radiant power 2854°K 200 D65 • CIE standard illuminant D65 A represents a phase of natural 150 daylight with a correlated color temperature of 6504°K 100 CIE standard illuminants B and C were intended to represent direct sunlight with a correlated color temperature of 4874°K resp. 6774°K. They are 50 being dropped because they are seriously deficient in the UV region (important for fluorescent materials) wavelength [nm] 0 300 350 400 450 500 550 600 650 700 750 800 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 68. 6.3 CIE standard sources 67 • Illuminant refers to a specific spectral radiant power distribution incident to the object viewed by the observer • Source refers to a physical emitter of radiant power, such as a lamp or the sun and sky • CIE illuminant A is realized by a gas-filled coiled-tungsten filament lamp operating at a correlated color temperature of 2856°K • There are no artificial sources for illuminant D65, due to the jagged spectral power distribution. However, some sources qualify as daylight simulators for colorimetry • For more information see http://www.communities.hp.com/online/blogs/mostly_color/archive/2007/06/22/ HPPost3682.aspx G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 69. 7 Measuring color 68 • There are no filters that approximate well the color matching functions • There are no artificial sources for the popular illuminants D65 and D50 • Today’s hardware situation has changed dramatically • Embedded processors are inexpensive • Holographic gratings are inexpensive • Light sources are highly efficient • CCD sensors have much less dark noise • It is better to perform spectral measurements and let the instrument do the colorimetry • Spectroradiometer: determine the reflected SPD • Spectrophotometer: determine the reflectance curve • see drawing on page 64 (Illumination) • Because they are a closed system, spectrophotometers are very reliable G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 70. 7.1 Trusting your instrument 69 Sooner or later all users enter a deep trust crisis in their instruments. Some survival tips: • Illuminate your work area with a source simulating your target illuminant • see what the instrument “sees” • Compact spectrophotometers have a very small geometry; perpendicularity between optical axis and sample, as well as distance to the sample are critical • maintain an uncluttered work space • The instrument’s light source generates heat, which increases dark current noise in the CCD and causes geometric deformations in the grating • wait between measurements • recalibrate • at each session start • after each pause • after a long series of measurements, • when the ambient temperature has changed by more than 5˚C G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 71. 7.2 Calibration 70 White calibration: adjusts computational parameters so the calculated tile’s reflectance curve is the same as the absolute reflectance curve • do it often Absolute certification: verifies that the measured color of the tile is within the tolerance (e.g. 0.6 ΔE units) from the absolute color of the tile • important for agreement between laboratories Relative certification: verifies if the measured color of the tile is within the tolerance (e.g. 0.3 ΔE units) from the initial color of the tile with the same instruments • important for reproducibility Collaborative testing: verifies that the entire color measurement procedure is in agreement with outside laboratories Collaborative Testing Services Inc, 21331 Gentry Drive, Sterling, VA 20166, 571-434-1925 http://www.collaborativetesting.com/ G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 72. 7.3 Effect of variability 71 • A measurement is never perfect • The effect of variability of color measurement is reduced by using multiple measurements • How many measurements should I make and average? • Rule of thumb: 10× for each variability parameter • instrument’s variability: measure each spot — 10× • sample uniformity: repeat at several locations — 100× • sample variability: repeat for several samples — 1000× • … • Follow ASTM standard practice E 1345 – 90 to determine how many measurements are necessary in each case • ASTM, 100 Barr Harbor Drive, West Conshohoken, PA 19428, 610-832-9585, http://www.astm.org • Improve all process aspects to minimize the required number of measurements • ISO 9001 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 73. 7.4 Geometries of illumination and 72 viewing • On a glossy surface there are mirror-like (specular) reflections • There are more reflections in the case of diffuse light sources • Since the color of the illuminant is white, specular reflections add white, with the effect of desaturating the color • Non-metallic glossy surfaces look more saturated in directional than in diffuse illumination • Matte surfaces scatter the light diffusely — matte surfaces usually look less saturated than glossy surfaces • Most surfaces are between glossy and matte • Diffuse illumination is provided by integrating spheres • usually they are provided with gloss traps • Instruments with 45/0 and 0/45 geometry are less critical • ASTM recommendation for partly glossy samples: • use the geometry that minimizes surface effects (usually the one that gives lowest Y and highest excitation purity) • 45/0 geometry gives rise to polarization problems G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 74. 8 Color reproduction 73 In most cases, color reproduction is simple and inexpensive because of metamerism Spectral color reproduction: equality of spectral reflectance or SPD • rarely needed • paint samples, metamerism assessment Colorimetric reproduction: equality of chromaticities and relative luminances • useful when viewing conditions are the same and light source is the same Exact reproduction: equality of chromaticities, absolute & relative luminances • useful when viewing conditions are identical G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 75. 8.0.1 Reproduction modes (cont.) 74 Equivalent reproduction: same appearance of chromaticities, absolute & relative luminances • useful when the luminance level is the same Corresponding reproduction: same appearance of chromaticities and relative luminances when the luminance levels are the same • current focus of research in color reproduction; CIECAM Preferred reproduction: achieve more pleasing reproduction of memory colors by departing from equality of appearance G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 76. 8.1 Additive and substractive color 75 Mixing of colored lights vs. mixing of colorants • Additive color: start with black and add primaries • red green blue • Substractive color: start with white and substract complements of primaries • cyan magenta yellow G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 77. 8.1.1 The additive method 76 • Probable sensitivity absorbance S-cone 1.0 curves of the human eye M-cone and the three best lights 0.8 for additive color L-cone reproduction 0.6 Rod • Note the strong overlap 0.4 in the orange-yellow interval 0.2 • This means that correct color reproduction 0.0 nm 400 450 500 550 600 650 cannot be achieved with simple trichromatic methods, because there are always unwanted stimulations • Hence, the trivial idea of stimulating the cones independently does not work with a simple approach G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 78. 8.1.2 The subtractive method 77 • The additive method has two major disadvantages when the set-up is not light-emissive: • the required filters significantly reduce the brightness of the image • the reproduction of a mosaic can be tricky • It is easier to generate colors from a beam of white light and varying the proportions of reddish, green, and bluish parts • On top to the unwanted stimulations, there is a problem with unwanted absorptions, making the subtractive method even harder to master than the additive method 1.0 0.8 0.6 0.4 10% 50% 0.2 100% 0.0 400 450 500 550 600 650 700 1.2 1.0 0.8 0.6 10% 0.4 50% 0.2 100% 0.0 400 450 500 550 600 650 700 1.0 0.8 0.6 10% 0.4 50% 100% 0.2 0.0 400 450 500 550 600 650 700 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 79. 8.1.3 Dithering 78 • Color is a usually represented with at least 8 bits per channel, for 256 levels • Some devices can display less levels • mobile LCD displays often have only 6 bits per channel • most printers have only 1 bit per channel • Displays: temporal dithering • Printers: spatial dithering, a.k.a. halftoning G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 80. 8.2 Scan — think — print 79 • Because of the unwanted stimulations and absorptions, it is practically impossible to engineer a color reproduction system based on light and lenses producing satisfactory image quality • Because of the large amount of data and lengthy computations, digital systems are possible only slowly • Initially, closed proprietary solutions • Later, open solutions based on standards and a color management system • SWOP inks • ICC profiles G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 81. 8.2.1 Managed color reproduction 80 sRGB profile maker spectro- business negative YCC photometer TIJ printer PhotoCD CMYK scanner graphic arts CIELAB TIJ printer RGB ICC profile RGB graphic arts workstation scanner and archive digital positive proof printer AdobeRGB RGB CMYK Inte ICC profile digital rne camera display and platemaker or t softcopy direct press CIELAB+sRGB any ICC profile color rendering dictionary repository raster image (database) processor G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 82. 9 Milestones in color printing 81 30,000 BCE: hand is commonly used as a stencil by holding it against a cave wall and blowing powder on it 1457: Fust and Schöffer use colored metal plates to print the Psalterium with colored initial letters. They had to discover and solve the problems of color trapping and registration Breakthrough: mass-production of illuminated books 1580–1644: during the Ming dynasty, techniques are perfected for the mass-production of multicolored book illustrations ~1700: invention of the katagami stencil. The stencil’s loose elements are connected with silk wires fine enough that ink can flow around them, enabling the mass-production of fine illustrations. Ukiyo-e — pictures of the floating world G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 83. 9.0.1 Color printing milestones (cont.) 82 1719: Le Blon receives British patent 423 for inventing the trichromatic printing principle. Yellow, red, blue plus black for better gray balance and clean blacks 1797: Senefelder invents lithography, enabling the inclusion of a large number of illustrations in very long run books like the Encyclopédie 1816: Engelmann invents chromolithography; 6 to 19 partial colors, sometimes even 24 and 30 1816: Young invents color filters, which will allow to separate color images 1852: Fox Talbot invents concept of halftone screening 1879: Swan invents line screen 1888: Meisenbach invents crossline screen G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 84. 9.0.2 Color printing milestones (cont.) 83 1910: invention of the panchromatic film emulsion, allowing the use of Maxwell’s filters From here on all effort goes into color correction (masking) 1937: Neugebauer proposes an eight-color analytical method based on colorimetry 1948: Hardy and Wurzburg invent the scanner — electronic circuitry is used to determine the color correction in one single step. The 1941 Murray and Morse scanner just tried to simulate masking Hardy and Wurzburg’s solved the Neugebauer equations 1957: Patent 2,790,844 — early effort towards gamut mapping 1977: Ichiro Endo receives U.S. patent 4,723,129 for thermal ink jet technology 1987: Canon launches CLC-1 color copier G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 85. 10 Color image communication 84 Application Protocol Format Compression Color image G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 86. 10.1 Lossless coding 85 • Huffman coding • Arithmetic coding • LZ coding • LZW coding (USP 4,558,302) • Flate and deflate (IETF RFC 1951) • Binary image compression • Group 3 1-d (MH) and 2-d (MR) • ITU-T Rec. T.4 • Group 4 (MMR) • ITU-T Rec. T.6 • JBIG — progressive bi-level image compression • ISO 11544 / ITU-T Rec. T.82 • ITU-T Rec. T.85 — application profile for fax • ITU-T Rec. T.43 — bit-plane coding for color fax images using JBIG • JBIG2 — lossy/lossless coding for bi-level images • ISO 14492 / ITU-T Rec. T.88 • text halftone, and generic modes • lossless JPEG • lossless JPEG 2000 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 87. 10.2 Palette color 86 Counting colors • 24-bit pixels can represent 16 million colors • Humans can distinguish 10 million colors • A 2×3K image contains 6 million pixels • A 512×512 image contains 250 thousand pixels • A “typical” 5122 image has 26 thousand colors • One byte can represent 256 colors G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 88. 10.2.1 Color palettes (mapped color) 87 • Represent original colors by indices into a map with reduced set of colors (paint by numbers) • choose N colors (palette) • image dependent (adaptive) or image independent (fixed) • e.g., median cut • quantize (map) original to palette colors • use look-up table to map index to palette color • may use dither in palettized image quantize original index Q G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 89. 10.3 JPEG 88 • No color space specification • Baseline JPEG: 4 or less color components • Colorimetric color representation is possible • Full JPEG: 256 or less color components • Discrete spectral color representation is possible • Compression can be improved with chroma subsampling JPEG 2000 • Wavelet-based follow-on to JPEG • same committee, different contributors • Single compression architecture • continuous-tone and binary compression • lossy, lossless, and lossy-to-lossless coding • progressive rendering • 1–256 color (spectral) components G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 90. 10.4 Mixed Raster Content — background 89 T.6 T.4 black-and-white black-and-white MMR text and line text and line MH diagrams diagrams T.85 in1 out in1 out in2 in2 JBIG black-and-white text, halftones, stipples, line art, PSTN and so on Multiple, independent compression methods— T.42 T.43 each optimized for one JPEG JBIG kind of image content CIELAB CIELAB G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 91. 10.4.1 Mixed Raster Content — solution 90 black-and-white T.44 text & digrams as before, Mixed colored Raster text Content too interchange black-and-white text and line diagrams black-and-white text, halftones, stipples, line art, color text and in1 and so graphics on in2 out MRC is a method for using multiple compression methods in raster documents that contain multiple kinds of content G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 92. 10.4.2 Mixed Raster Content — overview 91 • MRC = Mixed Raster Content • multi-layer model for representing compound images • described in ITU-T Recommendation T.44 • originally proposed in joint Xerox/HP contribution • efficient processing, interchange and archiving of raster-oriented pages with a mixture of multilevel and bilevel images • Technical approach • segmentation of an image into multiple layers (planes), by image content • use spatial resolution, color representation and compression method matched to the content of each layer • Compound image architecture • framework for using compression methods • Performance • can achieve compression ratios of several 100 to 1 on typical documents G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 93. 10.4.3 Mixed Raster Content — model 92 Image 3-layer model black-and-white text & digrams colored text • Foreground • multilevel, e.g., text color bla • JBIG @ 12 bpp, 100 dpi ck red • Mask • bilevel, e.g., text shape bla tex ck-a • MMR @ 1 bpp, 400 dpi t n co & dig d-wh lor i ed rams te tex • Background t • multilevel, e.g., contone im. • JPEG @ 24 bpp, 200 dpi Image = M • FG + M’ • BG G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 94. 10.4.4 Internet fax 93 What is it? • Store-and-forward Internet fax • scanned document transmission using e-mail attachments • ITU-T standards and IETF protocols • uses ESMTP with delivery confirmation and capabilities exchange • ITU-T Recommendation T.37 — approved September 1999 • references IETF standards • requires use of TIFF-FX • Simple Mode — TIFF-FX Profile S: April 1999 • minimal b&w with no delivery confirmation or capability exchange • Full Mode — TIFF-FX all profiles: September 1999 • range of b&w and color with delivery confirmation and capability exchange G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 95. 10.4.4.1 Internet fax — configurations 94 Internet all-in-one workstation PSTN on/off ramp fax G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 96. 10.4.5 IPP — Internet Printing Protocol 95 What is it? • Firewall problem • IETF standard developed with help from the Printer Working Group • Client-server protocol for distributed printing on the Internet • intended to replace LPR/LPD • Uses HTTP 1.1 POST application protocol • Internet media type: application/ipp G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 97. 10.4.5.1 IPP — Internet Printing Protocol 96 Sample configurations Client to printer IPP client IPP object Client to server IPP client IPP object G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 98. 10.5 Document ecosystems 97 Seamless office imaging • Scanners, copiers, connected to Ethernet instead of computer • Documents distributed via e-mail, fax servers, remote printers, or ISV applications HP 9100C Imaging Service Application write read TCP/IP image + metadata NOTIFY.DAT HP 9100C Windows Shared Application Digital Sender Server Disk Server G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 99. 11 Color appearance modeling 98 • Recommended model: CIECAM02 • Do not use an appearance model when • stimulus specification is simple (CIELAB, sRGB, …) • simple color tolerances (CIE94) • only one viewing condition • it is not clear it will help • What they allow you to do • map from measurements to color names • predict color matches across viewing conditions • render color across media • gain a deeper understanding of color • no metric for color differences G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 100. 11.1 Cognitive context 99 stimulus detectors early mechanisms pictorial register color edges contour motion depth … context parameters chroma etc. hue Color lexicon lightness chroma internal etc. color space amber hue lightness action color name apparent color representation G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 101. 11.2 CIECAM02 100 • Conditions modeled • adaptation • discounting the illuminant • surround effects • Predictions missing from the model • rod contributions • color difference metric • constant hue lines • Helson-Judd effect • Helmholtz-Kohlrausch effect • Graphical representation • CIECAM02 is represented in cylindrical coordinates • lightness J • chroma C • hue h • trigonometric transformation necessary for plots • Includes the 5 years of revisions since CIECAM97s G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 102. 11.3 The color selection problem 101 Surround 10º Background Color considered 2º Adapting field Proximal field • This user interface problem cannot be solved without color appearance model • Currently users converge towards their intended rendering by trial and error G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 103. 11.4 The gamut mapping problem 102 b* Printer a* Measure original Monitor Compute appearance CG Image Gamut compression Modify appearance (L*C*hab) Compute colorant quantities G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color