Understanding Color
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SPIE short course on color science (introductory). This is an old version, a newer one is called "Understanding Color 2010"

SPIE short course on color science (introductory). This is an old version, a newer one is called "Understanding Color 2010"

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Understanding Color Presentation Transcript

  • 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
  • 104. 12 Color image communication synopsis 103 Application Internet Internet Web Browsers Image Transfer Fax Printing Protocol HTTP *TP IIP *TP ESMTP IPP Format HTML FlashPix JP2 TIFF-FX other formats Profile Profile via plug-ins C M supported e.g., PDF, document TIFF, SVG GIF PNG JFIF formats Compression LZW flate JPEG JPEG JPEG 2000 MRC palette JPEG Color Space Application Protocol Format RGB sRGB ICC YCbCr sRGB Photo- sRGB, simple CIELAB binary Compression profile YCC Gray ICC profile Color image G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 105. 13 Color space support 104 by file format LZW flate JPEG GIF device RGB n/a n/a PNG n/a device RGB, sRGB n/a JFIF n/a n/a YCbCr FlashPix n/a n/a PhotoYCC, sRGB TIFF-FX Profile C n/a n/a CIELAB PDF dev. RGB, dev. CMYK, cal. RGB, CIELAB, XYZ, ICC profiles • LZW, flate for text, graphics, and indexed images • JPEG for images G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 106. 14 Conclusions 105 • What you should take home from this course: • a more realistic expectation from color reproduction • color is more an art than a science • practice, practice, practice • develop your intuition • color reproduction algorithms could not be patented before the late 80s • prior art is in the literature, not in the USPTO • algorithms often wrapped in an apparatus • how to interpret the result of a color measurement • how to trust your instrument • Acknowledgements: • collaboration with Robert R. Buckley, Xerox Corporation • metamerism test kits donated by X-Rite • www.hpl.hp.com/personal/Giordano_Beretta/ G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 107. 15 Bibliography 106 • R.S. Berns. Billmeyer and Saltzman’s Principles of Color Technology. 3rd edition, John Wiley & Sons, New York, 2000 • CIE Publ. No. 17.4. International Lighting Vocabulary. 1987 • J. Davidoff. Cognition through Color. The MIT Press, Cambridge, 1991 • M.D. Fairchild. Color Appearance Models. 2nd edition, John Wiley & Sons, Hoboken, 2005 • G.A. Gescheider. Psychophysics. Lawrence Erlbaum, Hillsdale, 1985 • E.J. Giorgianni and Th.E. Madden. Digital Color Management. Prentice Hall PTR, 1998, ISBN: 0201634260 • R.W.G. Hunt. Measuring Colour. 3rd edition, Fountain Press, Kingston-upon- Thames, 1998 • R.W.G. Hunt. The Reproduction of Colour in Photography, Printing & Television. 6th edition, John Wiley & Sons, Hoboken, 2004, ISBN: 0-470-02425-9 • R.S. Hunter and R.W. Harold. The Measurement of Appearance. 2nd edition, John Wiley & Sons, New York, 1987 • P.K Kaiser and R.M. Boynton. Human Color Vision, Second Edition. Optical Society of America, 1996 (original publication 1979) • H.R. Kang. Color Technology for Electronic Imaging Devices. SPIE, Bellingham, 1997 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color
  • 108. • H.R. Kang. Digital Color Halftoning. SPIE, Bellingham, 1999 • H.R. Kang. Computational Color Technology. SPIE, Bellingham, 2006, ISBN: 0- 107 8194-6119-9 • Helga Kolb et al. Webvision—The Organization of the Retina and Visual System. http://webvision.med.utah.edu/ • R.G. Kuehni. Color: An Introduction to Practice and Principles. John Wiley & Sons, Chichester, 2000 • A. Nemcsics. Colour Dynamics—Environmental Colour Design. Akadémiai Kiadó, Budapest, 1993 • R. Tilley. Colour and the Optical Properties of Materials. John Wiley & Sons, New York, 1987 • R.L. van Renesse. Optical Document Security. Artech House, Boston, 2005 • H. Widdel and D.L. Post, Editors. Color in Electronic Displays. Plenum Press, New York, 1992 • S.J. Williamson and H.Z. Cummins. Light and Color in Nature and Art. John Wiley & Sons, New York, 1983 • G. Wyszecki and W.S. Stiles. Color Science: Concepts and Methods, Quantitative Data and Formulæ. 2nd edition, John Wiley & Sons, New York, 2000, ISBN: 0-471-39918-3 • H. Zollinger. Color: A Multidisciplinary Approach. Helvetica Chimica Acta, Zurich, 1999, ISBN: 3-906390-18-7 G.B. Beretta Alexandria, 6 June 2008 SC076 — Understanding Color