This is the subject slides for the module MMS2401 - Multimedia System and Communication taught in Shepherd College of Media Technology, Affiliated with Purbanchal University.
This is the subject slides for the module MMS2401 - Multimedia System and Communication taught in Shepherd College of Media Technology, Affiliated with Purbanchal University.
This is the subject slides for the module MMS2401 - Multimedia System and Communication taught in Shepherd College of Media Technology, Affiliated with Purbanchal University.
This is the subject slides for the module MMS2401 - Multimedia System and Communication taught in Shepherd College of Media Technology, Affiliated with Purbanchal University.
Image Restitution Using Non-Locally Centralized Sparse Representation ModelIJERA Editor
Sparse representation models uses a linear combination of a few atoms selected from an over-completed
dictionary to code an image patch which have given good results in different image restitution applications. The
reconstruction of the original image is not so accurate using traditional models of sparse representation to solve
degradation problems which are blurring, noisy, and down-sampled. The goal of image restitution is to suppress
the sparse coding noise and to improve the image quality by using the concept of sparse representation. To
obtain a good sparse coding coefficients of the original image we exploit the image non-local self similarity and
then by centralizing the sparse coding coefficients of the observation image to those estimates. This non-locally
centralized sparse representation model outperforms standard sparse representation models in all aspects of
image restitution problems including de-noising, de-blurring, and super-resolution.
Here I presented my presentation slide about color image processing.
In color image processing, an abstract mathematical model known as color space is used to characterize the colors in terms of intensity values. This color space uses a three-dimensional coordinate system. For different types of applications, a number of different color spaces exists. The saturation is determined by the excitation purity, and depends on the amount of white light mixed with the hue. A pure hue is fully saturated, i.e. no white light mixed in. Hue and saturation together determine the chromaticity for a given colour. Finally, the intensity is determined by the actual amount of light, with more light corresponding to more intense colours[1].
Achromatic light has no colour - its only attribute is quantity or intensity. Greylevel is a measure of intensity. The intensity is determined by the energy, and is therefore a physical quantity. On the other hand, brightness or luminance is determined by the perception of the colour, and is therefore psychological. Given equally intense blue and green, the blue is perceived as much darker than the green. Note also that our perception of intensity is nonlinear, with changes of normalised intensity from 0.1 to 0.11 and from 0.5 to 0.55 being perceived as equal changes in brightness[2].
Colour depends primarily on the reflectance properties of an object. We see those rays that are reflected, while others are absorbed. However, we also must consider the colour of the light source, and the nature of human visual system. For example, an object that reflects both red and green will appear green when there is green but no red light illuminating it, and conversely it will appear red in the absence of green light. In pure white light, it will appear yellow (= red + green).
full color,pseudo color,color fundamentals,Hue saturation Brightness,color model,RGB color model,CMY and CMYK color model,HSI color model,Coverting RGB to HSI, HSI examples
Similar to Multimedia color in image and video (20)
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
2. Color Science
Color Models in Images
Color Models in Video.
2
Out line
3. 4.1 Color Science4.1 Color Science
Light and Spectra
Light is an electromagnetic wave. Its color is characterized
by the wavelength content of the light.
Visible light is an electromagnetic wave in the 400nm-700
nm rang. most light we see is not one wavelength , it’s a
combination of many wavelengths.
SPD( Spectral Power Distribution)
The relative power in each wavelength interval for typical
daylight this type of curve is called SPD or Spectrum.
The symbol for wavelength is λ. This curve is called E(λ ).
Fig. 4.2 shows the relative power in each wavelength interval
for typical outdoor light on a sunny day. This type of curve is
3
5. 4.1 Color Science4.1 Color Science
Spectrophotometer: device used to measure visible
light, by reflecting light from a diffraction grating
(prism) (a ruled surface) that spreads out the different
wavelengths.
Figure 4.1 shows the phenomenon that white light
contains all the colors of a rainbow.
Visible light is an electromagnetic wave in the range 400
nm to 700 nm (where nm stands for nanometer, 10 9−
meters).
5
7. 7
Human Vision
The eye is basically just a camera
Each neuron is either a rod or a cone. Rods are not
sensitive to color.
Cones come in 3 types: Red ,Green and blue . Each
responds differently to various frequencies of light.
The color signal to the brain comes from the response
of the 3 cones to the spectral being observed .
10. 10
Spectral Sensitivity of the Eye
The eye is most sensitive to light in the middle of
the visible spectrum.
. The following fig4.3 shows the spectral
sensitivity functions of the cones and the luminous
–efficiency
function called V(λ) of the human eye.
11. SpectralSpectral Sensitivity of the EyeSensitivity of the Eye
It is usually denoted V (λ) and is formed as
the sum of the response curves for Red,
Green, and Blue.
11
12. Image FormationImage Formation
In most situations, we actually image light that is
reflected from a surface.
Surfaces reflect different amounts of light at
different wavelengths, and dark surfaces reflect less
energy than light surfaces.
Surface spectral reflectance functions S(λ) for
objects.
then the reflected light filtered by the eye’s cone.
Reflection is shown in Fig. 4.5 below.
12
14. 4.1 Color Science4.1 Color Science
14
Camera Systems
Camera systems are made in a similar fashion; a good camera
has three signals produced at each pixel location
(corresponding to a retinal position).
Analog signals are converted to digital, truncated to integers,
and stored. If the precision used is 8-bit, then the maximum
value for any of R,G,B is 255, and the minimum is 0.
The light entering the eye of the computer user is what the
screen emits – the screen is essentially a self-luminous
source. Therefore, we need to know the light E(λ)
entering the eye.
15. Gamma correctionGamma correction
Gamma: is the light emitted is in fact roughly proportional to the voltage
raised to power ,this power is called Gamma with symbol Y
(a) Thus, if the file value in the red channel is R, the screen emits light
proportional to R, with SPD equal to that of the red phosphor paint on
the screen that is the target of the red channel electron gun. The value of
gamma is around 2.2.
(b) It is customary to append a prime to signals that are gamma-corrected
by raising to the power before transmission. Thus we arrive at linear
signals:
15
4.1 Color Science4.1 Color Science
17. Gamma correctionGamma correction
The combined effect is shown in Fig. 4.7(b). Here, a ramp is shown in
16 steps from gray-level 0 to gray-level 255.
17
18. Gamma correctionGamma correction
A more careful definition of gamma recognizes that a simple power law
would result in an infinite derivative at zero voltage makes constructing
a circuit to accomplish gamma correction difficult to devise in analog.
18
20. Color-Matching FunctionsColor-Matching Functions
A color can be specified as the sum of three color .So colors form a
3 dimensional vector space .
The amounts of R, G, and B the subject selects to match each
single-wavelength light forms the color-matching curves .These are
denoted
The negative value indicates that some colors cannot be exactly
produced by adding up the primaries.
20
21. CIE chromaticity diagramCIE chromaticity diagram
Since the r(λ) color-matching curve has a negative lobe, a set of
fictitious primaries were devised that lead to color-matching functions
with only positives values.
(a) The resulting curves are shown in Fig. 4.10;,these are usually
referred to as the color-matching functions.
(b) They are a 3 *3 matrix away from curves, and are
denoted x(λ),y(λ),z(λ).
(c) The matrix is chosen such that the middle standard color-matching
function y(λ) exactly equals the luminous-efficiency curve V (λ)
shown in Fig. 4.3.
21
22. CIE chromaticity diagramCIE chromaticity diagram
For a general SPD E(λ), the essential colorimetric information
required to characterize a color is the set of tristimulus values X, Y , Z
defined in analogy to (Eq. 4.1) as(Y == luminance):
* from Eq(4.6) increasing the brightness of illumination increases
tristimulus value by scalar multiple
3D data is difficult to visualize, so the CIE devised a 2D diagram based
on the values of (X,Y,Z) triples implied by the curves in Fig. 4.10.
22
23. CIE chromaticity diagramCIE chromaticity diagram
We go to 2D by factoring out the magnitude of vectors (X,Y,Z); we
could divide by , but instead we divide by the
sum X +Y +Z to make the chromaticity:
This effectively means that one value out of the set (x, y , z)is
redundant since we have
23
24. CIE chromaticity diagramCIE chromaticity diagram
24
Effectively, we are projecting each tristimulus vector (X,Y,Z)
onto the plane connecting points (1,0, 0), (0,1,0), and (0,0,1).
Fig. 4.11 shows the locus of points for monochromatic light
25. CIE chromaticity diagramCIE chromaticity diagram
(a) The color matching curves each add up to the same
value the area under each curve is the same for each
of x(λ),y(λ),z(λ).
(b) For an E(λ) = 1 for all ,λ an “equi-energy white light"
chromaticity values are (1l3,1l3). Fig. 4.11 displays
a typical actual white point in the middle of the
diagram.
(c) Since , all possible
chromaticity values lie below the dashed diagonal line in
(Fig. 4.11).
25
26. Color monitor specificationsColor monitor specifications
• Color monitors are specified in part by the white point chromaticity
that is desired if the RGB electron guns are all activated at their
highest value .
• We want the monitor to display a specified white when
R`=G`=B`=1
Out –of –Gamut colors
For any (x,y) pair we wish to find that RGB triple giving the specified (x, y,z):
We form the z values for the phosphors , via
z = 1 x y− − and solve for RGB from the phosphor chromaticities.
We combine nonzero values of R, G, and B via
26
27. Out –of –Gamut colorsOut –of –Gamut colors
If (x y) [color without magnitude] is specified, instead of
derived as above, we have to invert the matrix of
phosphor (x, y, z) values to obtain RGB.
What do we do if any of the RGB numbers is negative?
that color, visible to humans, is out-of-gamut for our
display.
1. One method: simply use the closest in-gamut color
available, as in Fig. 4.13.
2. Another approach: select the closest complementary
color.
27
29. Out –of –Gamut colorsOut –of –Gamut colors
Grassman's Law: (Additive) color matching is linear. This
means that if we match color1 with a linear combinations
of lights and match color2 with another set of weights, the
combined color color1+color2 is matched by the sum of the
two sets of weights.
Additive color results from self-luminous sources, such as
lights projected on a white screen, or the phosphors glowing
on the monitor glass. (Subtractive color applies for printers,
and is very different).
29
30. White point correctionWhite point correction
Problems:
One deficiency in what we have done so far is that we need
to be able to map tristimulus values XY Z to device RGBs
including magnitude, and not just deal with chromaticity xyz.
To correct both problems, take the white point magnitude of
Y as unity: Y (white point) = 1 (4:11)
Now we need to find a set of three correction factors such
that if the gains of the three electron guns are multiplied by
these values we get exactly the white point XY Z value at
R = G = B = 1.
Suppose the matrix of phosphor chromaticities xr; xg ; ::: etc.
in Eq. (4.10) is called M . We can express the correction as
a diagonal matrix D = diag (d1; d2; d3) such that XY Z white
M D (1, 1,1)T (4:12)
30
31. XYZ to RGB transformXYZ to RGB transform
To transform XYZ to RGB calculate the
linear RGB required ,by inverting the
equ.
then make nonlinear signals via gamma
correction
31
32. Transform with Gamma correctionTransform with Gamma correction
Now the 3 × 3 transform matrix from XYZ to RGB is taken to be
T = M D (4:15)
32
34. L*a*b* (CIELAB) Color ModelL*a*b* (CIELAB) Color Model
A refined CIE model ,named CIE L*a*b in 1979
Luminance : L
Chrominance : a– ranges from green to red, b– ranges
from blue to yellow
Used by Photoshop
34
35. More color-coordinate schemesMore color-coordinate schemes
There are several other coordinate schemes in use to describe
color as humans perceive it ,whether gamma correction should or
not be applied
Schemes include:
a) CMY | Cyan (C), Magenta (M) and Yellow (Y ) color model;
b) HSL | Hue, Saturation and Lightness;
c) HSV | Hue, Saturation and Value;
d) HSI | Hue, Saturation and Intensity;
e) HCI | C=Chroma;
f) HVC | V=Value;
g) HSD | D=Darkness.
35
36. Munsell color naming systemMunsell color naming system
The idea is to set up approximately
perceptually uniform system of three
axes to discuss and specify color.
The axes are
value(black,white),Hue,chroma.
Value divided into 9 steps, hue is in 40
step . The circle’s radius with value.
36
37. Color Models in ImagesColor Models in Images
RGB Color Model for Displays
we expect to be able to use 8 bit per color channel for color that is
accurate enough. However, in fact we have to use about 12 bits per
channel to avoid an aliasing effect in dark image areas __contour
bands that result from gamma correction.
For images produced from computer graphics, we store integers
pro-portional to intensity in the frame bufer. So should have a
gamma correction LUT between the frame bufer
and the CRT.
If gamma correction is applied to floats before quantizing to integers,
before storage in the frame buffer, then in fact we can use only 8 bits
per channel and still avoid contouring artifacts.
37
38. Subtractive Color: CMY Color ModelSubtractive Color: CMY Color Model
So far, we have effectively been dealing only with
additive color. Namely, when two light beams
impinge on a target, their colors add; when two
phosphors on a CRT screen are turned on, their colors
add.
for example
red phosphor+ green phosphor= yellow phosphor
* red, green, and blue primaries, we used in monitor .
• subtractive color primaries are Cyan (C),
Magenta (M) and Yellow (Y ) inks
38
39. * In subtractive CMY system, black arises from
subtractive all the light by laying down inks with
C=M=Y=1
39
41. Transformation from RGB to CMYTransformation from RGB to CMY
Simplest model we can invent to specify what ink density
to lay down on paper, to make a certain desired RGB
color:
41
42. Undercolor Removal: CMYK SystemUndercolor Removal: CMYK System
Black ink is in fact cheaper than mixing colored inks to
make black, so a simple approach to producing sharper
printer color , remove it from the color proportions
and add it back as real this is called “undercolor
removal”
calculate that
The new specification of inks is thus:
42
43. Actual transmission curves overlap for the C, M, Y inks.
This leads to “crosstalk” between the color channels and
difficulties in predicting colors achievable in printing.
43
Printer GamutsPrinter Gamuts
44. Video Color Transforms
(a) Largely derive from older analog methods of
coding color for TV . Luminance is separated
from color information.
(b) This coding also makes its way into VHS
( VIDEO HOME SYSTEM) video tape coding in
these countries since video tape technologies
also use YIQ.
(c) In Europe, video tape uses the PAL or ECAM
coding which are based on TV that uses a matrix
transform called YUV.
44
Color models in videoColor models in video
45. YUV Color ModelYUV Color Model
• Human perception is more sensitive to luminance
(brightness )than chrominance (color) . Therefore
instead of separating colors on can separate the
brightness information from the color information Y
is luminance
• Y=0.299R+ 0.587G+0.114B
• Chrominance is defined as the difference between a
color and a reference white at the same luminance
it can be represented by U and V the color
differences
U=B`-Y` V=R`-Y`
45
46. • Eye is most sensitive to Y. therefore any
error in the resolution of the luminance (Y)
is more important the chrominance (U,V)
values.
• In PAL,5(OR 5.5)MHz is allocated to Y ,1,3
MHz to U and V
46
YUV Color ModelYUV Color Model
48. YIQ Color ModelYIQ Color Model
YIQ is used in NTSC color TV broadcasting.
Again, gray
pixels generate zero (I,Q) chrominance signal.
Although U and V nicely define the color
differences . They do not align with the desired
human perceptual color sensitivities. Hence , I
and Q are used instead
(a) I and Q are a rotated version of U and V .
48
49. YIQ Color ModelYIQ Color Model
(b) Y ' in YIQ is the same as in YUV,U and V are
rotated by 33:
(c) This leads to the following matrix transform:
49
50. YIQ Color ModelYIQ Color Model
(d) Fig. 4.19 shows the decomposition of the same
color image as a bove,into YIQ components.
50
51. YCbCr Color ModelYCbCr Color Model
The Rec. 601 standard for digital video uses
another color space, Y CbCr, often simply written
YCbCr -- closely related to the YUV transform.
(a) YUV is changed by scaling such that Cb is U,
but with a coefficient of 0.5 multiplying B'. In
some software systems, Cb and Cr are also
shifted such that values are between 0 and 1.
(b) This makes the equations as follows:
51
52. (d) In practice, however, Recommendation 601 species 8-
bit coding, with a maximum Y ' value of only 219, and a
minimum of +16. Cb and Cr have a range of -+112 and
offset of +128. If R', G', B‘ are floats in [0..+ 1], then we
obtain Y ', Cb, C rin [0.. 255] via the transform:
52
53. YCbCr Color ModelYCbCr Color Model
(f) The YCbCr transform is used in JPEG
image compression and MPEG video
compression.
53
ان الالوان المعرفه بالثلاثية المعرفه (ص ش ض) او (ص ش ق ) تعتمد على الالوان الاحمر واخضر والازرق للجهاز او الفضاء اللوني الخاص وعلى تصحيح كاما (gamma correction)
المستخدم لتمثيل كميات من الالوان الاولية
*The RGB numbers in an image file are converted back to analog and dlive the electron guns
in the cathode ray tube (CRT). Electrons are emitted proportional to the dliving voltage,
and we would like to have the CRT system produce light linearly related to the voltage.
Unfortunately, it turns out that this is not the case. The light emitted is actually roughly
proportional to the voltage raised to a power; this power is called &quot;gamma&quot;, with symbol y.
CIE defined three standard primaries (X,Y,Z). The Y primary was intentionally chosen to be identical to the luminous-efficiency function of human eyes.
Grassmans law: additive color matching is linear .this means that if we match color1 with a linear combinations of lights and match color2 with another set of weights the combined color color1=color2 is matched by the sum of the tow sets of weights.
* Additive color result from self –luminous source