RGB Models human visual system? Gives an absolute color description? Models color similarity? Linear...

RGB

• Models human visual system?

• Gives an absolute color description?

• Models color similarity?

• Linear model?

• Convenient for color displays?

RGB

• Models human visual system

• Gives an absolute color description

• Models color similarity

• Linear model

• Convenient for color displays

Spectra

• Light reaching the retina is characterized by spectral distribution, i.e. (relative) amount of power at each wavelength.

• Each kind of cone (S,M,L) responds differently.

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50

100

150

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Spectral Distribution of daylight at various color temperatures

T=25000R

ela

tive

Po

we

r

T=6504(CIE D65)

T=5003(CIE D50)

T=4000

Sources of colored light used in modern fireworks.

• Yellow Sodium D-line 589 nm

• Orange CaCl 591- 599 nm603-608 nm

• Red SrCl 617-623 nm 627-635 nm

640-646 nm

• Green BaCl 511-515 nm524-528 nm530-533 nm

• Blue CuCl 403-456 nm,

CorneaLens

Pupil

Iris

Retina

Fovea

Optic nerve

Ganglion

Bipolar

Amacrine

Rod Cone

Epithelium

Optic nerve

Retinal cross section

Light

Horizontal

Photoreceptors

• Cones - – respond in high (photopic) light– differing wavelength responses (3 types)– single cones feed retinal ganglion cells so give

high spatial resolution but low sensitivity– highest sampling rate at fovea

Photoreceptors

• Rods– respond in low (scotopic) light– none in fovea– one type of spectral response– several hundred feed each ganglion cell so give

high sensitivity but low spatial resolution

Optic nerve

• 130 million photoreceptors feed 1 million ganglion cells whose output is the optic nerve.

• Optic nerve feeds the Lateral Geniculate Nucleus approximately 1-1

• LGN feeds area V1 of visual cortex in complex ways.

Rods and cones

• Rods saturate at 100 cd/m2 so only cones work at high (photopic) light levels

• All have same spectral sensitivity

• Low light condition is called scotopic

• Three cone types differ in spectral sensitivity and somewhat in spatial distribution.

Cones

• L (long wave), M (medium), S (short)– describes sensitivity curves.

• “Red”, “Green”, “Blue” is a misnomer. See spectral sensitivity.

350 400 450 500 550 600 650 7000

0.2

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1

wavelength (nm)

Con

e re

spon

se

Cone Spectral Responses

Trichromacy

• Helmholtz thought three separate images went forward, R, G, B.

• Wrong because retinal processing combines them in opponent channels.

• Hering proposed opponent models, close to right.

Opponent Models

• Three channels leave the retina:– Red-Green (L-M+S = L-(M-S))– Yellow-Blue(L+M-S)– Achromatic (L+M+S)

• Note that chromatic channels can have negative response (inhibition). This is difficult to model with light.

Adaptation

• Luminance adaptation allows greater sensitivity but over narrow ranges

• Chromatic adaptation supports color constancy by compensating for changes in illuminating spectra.

100

10

1.0

0.1

0.001

-1 0 1 2

Log Spatial Frequency (cpd)

Con

tras

t Sen

siti

vity

Luminance

Red-Green

Blue-Yellow

Weber Fraction

• I/I = c, I = perceived change

• log I = log I + log c perceived change vs I

• log I = log I + yields

• I = c I power law

• Many perceptual responses follow power laws with <1, i.e. compressive non-linearity

Other non-linearities

•

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20

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40

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80

90

100

Stimulus P

Re

spo

nse

R N=1

N=2

N=3

Naka-Rushton Function

R(P)= M PN / sN + PN)

s=50 M=100

Color matching

• Grassman laws of linearity:()(((

• Hence for any stimulus s() and response

r(), total response is integral of s() r(), taken over all or approximately s()r()

Primarylights

Test light

Bipartitewhitescreen

Surround field

Test light Primary lights

Subject

Surround light

Color matching

• M() = R*R() + G*G() + B*B()

• Metamers possible

• good: RGB functions are like cone response

• bad: Can’t match all visible lights with any triple of monochromatic lights. Need to add some of primaries to the matched light

Primarylights

Test light

Bipartitewhitescreen

Surround field

Test light Primary lights

Subject

Surround light

350 400 450 500 550 600 650 700 750-0.5

0

0.5

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2

2.5

3

3.5Stiles and Burch 1959 10-degree bipartite field color matching functions

primary lights at 645.2 nm 525.3 nmand 444.4 nm

b10() g10()

r10()

Color matching

• Solution: XYZ basis functions

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y x

CIE 1931 standard colorimetric observer color matching functions

Tris

timul

us v

alue

wavelength (nm)

Color matching

• Note Y is V()

• None of these are lights

• Euclidean distance in RGB and in XYZ is not perceptually useful.

• Nothing about color appearance

CIE L*a*b*

• Normalized to white-point

• L* is (relative) ligntness

• a* is (relative) redness-greeness

• b* is (relative) yellowness-blueness

• C* = length on a*-b* space is chroma, i.e. degree of colorfulness

• h = tan-1(b*/a*) is hue

CIE L*a*b*, L*u*v*

• Euclidean distance corresponds to judgements of color difference, especially lightness

• Somewhat realistic nonlinearities modeled

• Lightness.m

• colorPatch.m - matlab image repn.

• umbColormatching.m

Color Appearance

Absolute

Brighness

Colorfulness

Relative

Lightness

Chromarel to white point“colorfulness/brightness(white)”

Saturationrel to own brightness“colorfulness/brightness”

Photoshop Calibration

• File->Color->RGB

• RGB space:– Gamma– White point– Primaries

• Reset to sRGB!!!

Photoshop color picker

• Examine planes of fixed– hue– saturation– lightness– L*– a*– b*

b*

darkdark

red

yellow

blue

green

light

a*

CIE Lab space

xyz2displayrgb

• SPD of color [r,g,b] :

xyz2displayrgb


phosphor

xyz2displayrgb


phosphor*[r,g,b]’

xyz2displayrgb


phosphor*[r,g,b]’

• XYZ tristimulus values

xyz2displayrgb


phosphor*[r,g,b]’

• XYZ tristimulus values xyz’=xyzbar’*phosphor*[r,g,b]’

xyz2displayrgb


phosphor*[r,g,b]’


• [r,g,b]’=

xyz2displayrgb


phosphor*[r,g,b]’


• [r,g,b]’= inv(xyzbar’*phosphor)*xyz’

xyz2displayrgb


phosphor*[r,g,b]’


• [r,g,b]’= inv(xyzbar’*phosphor)*xyz’

mon2XYZ

xyz2displayrgb


phosphor*[r,g,b]’


• [r,g,b]’= inv(xyzbar’*phosphor) *xyz’

xyz2displayrgb

Viewing Conditions

• Illuminant matters. Table 7-1 shows E* using two different illuminants.

• E* <= 2.5 is typically deemed a match.

• On the midterm: using chromaticities for Munsell principal hues, calculate E* for the hues with Wandell monitor whitepoint and D65

Viewing Modes

• Viewing mode = to what we attribute color

• Illuminant: illuminating light is colored

• Illumination: prevailing changes to the illuminant, e.g. shading from obstruction

• Surface: color belongs to the surface

• Volume: color belongs to the volume

• Aperture: pure color absent an object

Adaptation

• Light adaptation - quick

• Dark adaptation - slow

Chromatic Adaptation

• At all levels: cone, other retinal layers, LGN, cortex; including opponent mechanisms (e.g. green flash)

• Subserves discounting the illuminant when illuminant is spatially uniform

Adaptation mechanisms

• Neural gain control: reduced sensitivity at high input, increased at low input.– For cones this is photochemical dyanmics,

further up it is neurochemistry dynamics

• Temporal mechanisms -evidence for cortical adaptation mechanisms. (e.g. waterfall illusion).

Chromatic adaptation models

• vonKries: chromatic adaptation is– cone mediated– independent mechanisms in L,M,S– linear

• All are slightly wrong, but a good place to start.


• three independent gain controls:

La = kLL

Ma=kMM

Sa=kSS

• L = L-cone response, La = adapted response of L cones, etc


• Choice of gain control parameters depends on model. Often simply defined to guarantee adapted response is 1 at max of unadapted response or at scene-white

kL= 1/Lmax or kL= 1/Lwhite

so L max a = kL Lmax = 1, etc.


• If have two viewing conditions and M is transform for CIE XYZ to cone responses then can convert from adaptation in one condition to adaptation in the other by


Conversion from one adaptation to another

X1 Lmax2 0 0 1/Lmax1 0 0 X1

X2 = M-1 0 Mmax2 0 0 1/ Mmax1 0 M X2

X3 0 0 Smax2 0 0 1/ Smax1 X3

See Figure 9.2 for prediction of such a model

Non-linear chromatic adaptation models

• Nayatani: adds noise and power law in brightness.

• La = aL((L+Ln)/(L0+Ln))L etc.

• La : adapted L cone response

• Ln : noise signal; L0 : response to adapting field

• aL : fit from a color constancy hypothesis

Nayatani Color Appearance Model

• Model components– Nonlinear chromatic adaptation– One achromatic, two chromatic color opponent

channels weighted by cone population ratios


• Model outputs– Brightness as linear function of adapted cone

responses (which are non-linear!)– Lighness: achromatic channel origin translated

to black=0, white = 100– Brightness of “ideal white” (=perfect reflector)– Hue angle (from the chromatic channels)


• Model outputs– Hue quadrature: interpolation between 4 hues defined

by chromatic channels red (20.14), yellow (90 .00), green (164.25), blue (231.00)

– Saturation: depends on hue and luminance (predicts changes of chromaticity with luminance)

– Chroma = saturation*lightness

– Colorfullness: Chroma*brightness of ideal white.

Nayatani Color Appearance Model Advantages

• Invertible for many outputs, i.e. measure output quantities, predict inputs

• Accounts for changes in color appearance with chromatic adaptation and luminance

Nayatani Color Appearance Model Weaknesses

• Doesn’t predict:– Effects of changes in background color or

relative luminance– incomplete chromatic adaptation– cognitive discounting the illuminant– appearance of complex patches or background– mesopic color vision

Color Appearance

Absolute

Brighness

Colorfulness

Relative

Lightness

Chromarel to white point“colorfulness/brightness(white)”

Saturationrel to own brightness“colorfulness/brightness”

Hunt Color Appearance Model

• Inputs– chromaticity of adapting field– chromaticity of illuminant– chromaticity and reflectivity of

• background

• proximal field (up to 2° from stimulus)

• reference white


• Inputs– absolute luminance of

• reference white

• adapting field

– scotopic luminance data– parameters for chromatic and brightness

induction


• Properties– Non-linear responses– Models incomplete chromatic adaptation– Chromatic adaptation constants depend on

luminance– Models saturation– Models brightness, lightness, chroma and

colorfulness


• Good:– Predicts many color appearance phenomena– Useful for unrelated or related colors– Large range of luminance levels of stimuli and

background

• Bad:– Complex, computationally expensive– Not analytically invertible

Testing Color Appearance Models

• Qualitative tests

• Corresponding colors data (colors which appear the same when viewed under different conditions)

• Magnitude estimation tests

• Psychophysics

Testing Color Appearance Models- Qualitative Tests

• Predictions of color appearance phenomena, e.g. illuminant effects– Comparisons with color order systems

• e.g. Helson-Judd effect: perceived hue of neutral Munsell colors is not neutral under strong chromatic illumination but depends on hue of illuminant and relative brightness of test to background. Hunt model successfully predicts, von Kriess model does not.


• Magnitude Estimation of appearance attributes

• Comparisons with color order systems– e.g. Helson-Judd effect: perceived hue of

neutral Munsell colors is not neutral under strong chromatic illumination but depends on hue of illuminant and relative brightness of test to background.


• Adjust parameters to predict constancies in standard color order systems (e.g. constant L*a*b* chroma of Munsell colors), then test model for related properites (e.g. hue shift under luminance change).

• Predict complex related colors phenomena, e.g. local vs. global color filtering.

Testing Color Appearance Models- Corresponding Colors

• Corresponding colors: two different colors, C1, C2 which appear the same for two different viewing conditions V1, V2

• Test model by transforming C1 to V2.

• Importance: correcting images made under assumption of V1 but actually produce under V2, e.g. photos under D65 vs F vs A

Testing Color Appearance Models- Magnitude Estimation

• Observers assign numerical values to color appearance attributes

• Examples of results:– Background and white point have most influence

of colorfulness, lightness, hue– Magnitude estimation of lightness predicted best

by Hunt, next by CIELAB, then Nayatani– Estimation of colorfulness badly predicted by all

models

Testing Color Appearance Models- Magnitude Estimation

• Observers assign numerical values to color appearance attributes

• Examples of results:– Estimation of hue predicted best for Hunt

model, which was revised as suggested by experiments.

– etc. See Chapter 15, Fairchild

Testing Color Appearance Models- Pyschophysics

• Techniques starting with paired quality judgements can lead to a precise interval scale. (This is the way eyeglasses are prescribed.)

• Good for predicting media changes.

• (Review Fairchild 15.7)

MacAdam Ellipses

• JND of chromaticity

• Bipartite equiluminant color matching to a given stimulus.

• Depends on chromaticity both in magnitude and direction.

MacAdam Ellipses

• For each observer, high correlation to variance of repeated color matches in direction, shape and size– 2-d normal distributions are ellipses– neural noise?

• See Wysecki and Styles, Fig 1(5.4.1) p. 307

MacAdam Ellipses

• JND of chromaticity – Weak inter-observer correlation in size, shape,

orientation. • No explanation in Wysecki and Stiles 1982

• More modern models that can normalize to observer?

MacAdam Ellipses

• JND of chromaticity – Extension to varying luminence: ellipsoids in

XYZ space which project appropriately for fixed luminence

MacAdam Ellipses

• JND of chromaticity – Technology applications:

• Bit stealing: points inside chromatic JND ellipsoid are not distinguishable chromatically but may be above luminance JND. Using those points in RGB space can thus increase the luminance resolution. In turn, this has appearance of increased spatial resolution (“anti-aliasing”)

• Microsoft ClearType. See http://www.grc.com/freeandclear.htm and http://www.ductus.com/cleartype/cleartype.html

Complementary Colors

• Colors which sum to white point are called complementary colors

• a*c1+b*c2 = wp

• Some monochromatic colors have complements, others don’t. See ComplementaryColors.m

• Complements may be out of gamut. See Photoshop.

Printer/monitor incompatibilities

• Gamut– Colors in one that are not in the other– Different whitepoint– Complements of one not in the other

• Luminance ranges have different quantization (especially gray)

Photography, Painting

• Photo printing is via filters.

• Really multiplicative (e.g. .2 x .2 = .04) but convention is to take logarithm and regard as subtractive.

• Oil paint mixing is additive, water color is subtractive.

Printing

• Inks are subtractive– Cyan (white - red)– Magenta (white - green)– Yellow (white - blue)

• In practice inks are opaque, so can’t do mixing like oil paints.

• May use black ink on economic and physical grounds

Halftoning

• The problem with ink: it’s opaque

• Screening: luminance range is accomplished by printing with dots of varying size. Collections of big dots appear dark, small dots appear light.

• % of area covered gives darkness.

Halftoning references

• A commercial but good set of tutorials• Digital Halftoning, by Robert Ulichney,

MIT Press, 1987

• Stochastic halftoning

http://www.prepress.pps.com/mem/lib/ptr/escoriitr.html

http://mitpress.mit.edu/book-home.tcl?isbn=0262210096

http://mitpress.mit.edu/book-home.tcl?isbn=0262210096

http://www.sirius.com/~johnkyrk/conservatory.html

http://www.sirius.com/~johnkyrk/conservatory.html

Color halftoning

• Needs screens at different angles to avoid moire

• Needs differential color weighting due to nonlinear visual color response and spatial frequency dependencies.

Device Independence

• Calibration to standard space– typically CIE XYZ

• Coordinate transforms through standard space

• Gamut mapping

Device independence

• Stone et. al. “Color Gamut Mapping and the Printing of Digital Color Images”, ACM Transactions on Graphics, 7(4) October 1998, pp. 249-292.

• The following slides refer to their techniques.

Device to XYZ

• Sample gamut in device space on 8x8x8 mesh (7x7x7 = 343 cubes).

• Measure (or model) device on mesh.

• Interpolate with trilinear interpolation – for small mesh and reasonable function

XYZ=f(device1, device2, device3) this approximates interpolating to tangent.

XYZ to Device

• Invert function XYZ=f(device1, device2, device3)

– hard to do in general if f is ill behaved– At least make f monotonic by throwing out

distinct points with same XYZ.

• e.g. CMY device:– (continued)

XYZ to CMY

• Invert function XYZ=f(c,m,y)– Given XYZ=[x,y,z] want to find CMY=[c,m,y]

such that f(CMY)=XYZ– Consider X(c,m,y), Y(c,m,y), Z(c,m,y)– A continuous function on a closed region has

max and min on the region boundaries, here the cube vertices. Also, if a continuous function has opposite signs on two boundary points, it is zero somewhere in between.

XYZ to CMY

– Given X0, find [c,m,y] such that f(c,m,y) = X0

– if [ci,mi,yi] [cj,mj,yj] are vertices on a given cube, and U=X(c,m,y)- X0 has opposite sign on them, then it is zero in the cube. Similarly Y, Z. If find such vertices for all of X0,Y0,Z0, then the found cube contains the desired point. (and use interpolation). Doing this recursively will find the desired point if there is one.

Gamut Mapping

• Criteria:– preserve gray axis of original image– maximum luminance contrast– few colors map outside destination gamut– hue, saturation shifts minimized– increase, rather than decrease saturation– do not violate color knowledge, e.g. sky is blue,

fruit colors, skin colors

Gamut Mapping

• Special colors and problems– Highlights: this is a luminance issue so is about

the gray axis– Colors near black: locus of these colors in

image gamut must map into something reasonably similar shape else contrast and saturation is wrong

Gamut Mapping

• Special colors and problems– Highly saturated colors (far from white point):

printers often incapable.– Colors on the image gamut boundary

occupying large parts of the image. Should map inside target gamut else have to project them all on target boundary.

CRT

Printer

Gamuts

Gamut Mapping

• First try: map black points and fill destination gamut.

device gamutimage gamut

translate Bito Bddevice gamut

image gamut

translate Bito Bd

scale by csf


translate Bito Bd

scale by csf rotate


Gamut Mapping

Xd = Bd + csf R (Xi - Bi)

Bi = image black, Bd = destination black

R = rotation matrix

csf = contrast scaling factor

Xi = image color, Xd = destination color

Problems:Image colors near black outside of destination are

especially bad: loss of detail, hue shifts due to quantization error, ...

shift and scale alongdestination gray

Xd = Bd + csf R (Xi - Bi) + bs (Wd - Bd)

Fig 14a, bs>0, csf small, image gamut maps entirelyinto printer gamut, but contrast is low.

Fig 14b, bs=0, csf large, more contrast, more colors inside printer gamut, butalso more outside.

Saturation control

• “Umbrella transformation”[Rs Gs Bs] = monitor whitepoint

[Rn Gn Bn] new RGB coordinates such that Rs + Gs + Bs = Rn + Gn + Bn

and [Rn Gn Bn] maps inside destination gamut

First map R Rs+G Gs+B Bs to R Rn+G Gn+B Bn

Then map into printer coordinates

Makes minor hue changes, but “relative” colors preserved. Achromatic remain achromatic.

Projective Clipping

• After all, some colors remain outside printer gamut

• Project these onto the gamut surface:– Try a perpendicular projection to nearest

triangular face in printer gamut surface.– If none, find a perpendicular projection to the

nearest edge on the surface– If none, use closest vertex

Projective Clipping

• This is the closest point on the surface to the given color

• Result is continuous projection if gamut is convex, but not else.– Bad: want nearby image colors to be nearby in

destination gamut.

Projective Clipping

• Problems– Printer gamuts have worst concavities near

black point, giving quantization errors.– Nearest point projection uses Euclidean

distance in XYZ space, but that is not perceptually uniform.

• Try CIELAB? SCIELAB?

• Keep out of gamut distances small at cost of use of less than full printer gamut use.

Color Management Systems

• Problems– Solve gamut matching issues– Attempt uniform appearance

• Solutions– Image dependent manipulations (e.g. Stone)– Device independent image editors (e.g.

Photoshop) with embedded CMS– ICC Profiles

ICC Color Profiles

• International Color Consortium http://www.color.org.

• ICC Profile– device description text– characterization data– calibration data– invertible transforms to a fixed virtual color

space, the Profile Connection Space (PCS)

http://www.color.org/

Profile Connection Space

• Presently only two PCS’s: CIELAB and CIEXYZ

• Both specified with D50 white point

• Device<-->PCS must account for viewing conditions, gamut mapping and tone (e.g. gamma) mapping.

DVI color space(PCS)

Viewing-conditionindependent space

DVI color cpace

Output image and device

Input imageand device

DVI color space(e.g. XYZ)

Viewing-conditionindependent space

Chromatic adaptation and color appearance models

output devicecolorimetriccharacterization

Gamut mapping, tone control, etc



input devicecolorimetriccharacterization

Gamut mapping, tone control, etc

ICC Profiles

• Device profiles

• Colorspace profiles – data conversion

• Device Link profile – concatenated D1->PCS->D2

• Abstract profile – generic for private purposes, e.g. special effects

ICC Profiles

• Named color profile– Allows data described in Pantone system (and

others?) to map to other devices, e.g. view.– Supported in Photoshop

ICC Profile Data Tags

• Profile header tags:– administrative and descriptive

• Start of Header

• Byte count of profile

• Profile version number

• Profile or device class (input, display, output, link, colorspace, abstract, named color profile)

• PCS target (CIEXYZ or CIELab)


• Profile header tags:– ICC registered device manufacturer, model– Media attributes 64 attribute bits, 32 reserved

(reflective/transparent; glossy/matte. )– XYZ of illuminant– Rendering intent (Perceptual, relative

colorimetry, saturation, absolute colorimetry)

ICC Profile Rendering Intents

• perceptual: “full gamut of the image is compressed or expanded to fill the gamut of the destination device. Gray balance is preserved but colorimetric accuracy might not be preserved.” (ICC Spec Clause 4.9)

• saturation: “specifies the saturation of the pixels in the image is preserved perhaps at the expense of accuracy in hue and lightness.” (ICC Spec Clause 4.12)

• absolute colorimetry: relative to illuminant only

• relative colorimetry: relative to illuminant and media whitepoint


• Tone Reproduction Curve (TRC) tags:– grayTRC, redTRC, greenTRC, blueTRC

• single number (gamma) if TRC is exponential

• array of samples of the TRC appropriate to interpolation


• Mapping tags (“AtoB0Tag”, “BtoA0Tag”, etc.)

– Map between device and PCS– Includes 3x3 matrix if mapping is linear map of

CIEXYZ spaces, or lookup table on sample points if not.

ICC Profile Special Goodies

• Initimate with PostScript– Support for PostScript Color Rendering

Dictionaries reduces processing in printer– Support for argument lists to PostScript level 2

color handling

• Halftone screen geometry and frequency

• Undercolor removal

• Embedding profiles in pict, gif, tiff, jpeg,eps

JPEG DCT Quantization

• FDCT of 8x8 blocks. – Order in increasing spatial frequency (zigzag)

• Low frequencies have more shape information, get finer quantization.

• High’s often very small so go to zero after quantizing

– If source has 8-bit entries ( s in [-27, 27-1), can show that quantized DCT needs at most 11 bits (c in [-210, 210-1])

JPEG DCT Quantization

– Quantize with single 64x64 table of divisors– Quantization table can be in file or reference to

standard– Standard quantizer based on JND.– Note can have one quantizer table for each

image component– See Wallace p 12.

JPEG DCT IntermediateEntropy Coding

– Variable length code (Huffman):• High occurrence symbols coded with fewer bits

– Intermediate code: symbol pairs

– symbol-1 chosen from table of symbols si,j

• i is run length of zeros preceding quantized dct amplitude,

• j is length of huffman coding of the dct amplitude

– i = 0…15, j= 1…10, and s0,0=‘EOB’ s15,0 = ‘ZRL’

– symbol-2: Huffman encoding of dct amplitude

– Finally, these 162 symbols are Huffman encoded.

JPEG components

• Y = 0.299R + 0.587G + 0.114BCb = 0.1687R - 0.3313G + 0.5BCr = 0.5R - 0.4187G - 0.0813B

• Optionally subsample Cb, Cr – replace each pixel pair with its average. Not much loss

of fidelity. Reduce data by 1/2*1/3+1/2*1/3 = 1/3

• More shape info in achromatic than chromatic components. (Color vision poor at localization).

JPEG goodies

• Progressive mode - multiple scans, e.g. increasing spatial frequency so decoding gives shapes then detail

• Hierarchical encoding - multiple resolutions• Lossless coding mode• JFIF:

– User embedded data

– more than 3 components possible?

0 1

00s1

01s2

11s3

100s4

10

101

1011s6

1010s5

Huffman Encoding

0 1

00s1

01s2

11s3

100s4

10

101

1011s6

1010s5

1110101101100Traverse from root to leaf, then repeat:

11 1010 11 01 100

s3 s5 s3 s2 s4

Huffman Encoding

Charge Coupled Device (CCD)

< 10m x 10m

Silicon cells emit electrons when light falls on it

time

char

ge

charge

Charge Coupled Device (CCD)

lum

inan

ce

cell

< 10m x 10m

Y=0.299R + 0.587G +0.114B

Filters over cells

More green than red, blue

(For color tv and…?)

CCD Cameras

• Good links:– http://denton.chem.arizona.edu/ccd/

• Some device specs:– http://www.MASDKODAK.com/

http://denton.chem.arizona.edu/ccd/











http://www.masdkodak.com/

Color TV

• Multiple standards - US, 2 in Europe, HDTV standards, Digital HDTV , Japanese analog.

• US: 525 lines (US HDTV is digital, and data stream defines resolution. Typically MPEG encoded to provide 1088 lines of which 1080 are displayed)

http://www.atsc.org/

NTSC Analog Color TV

• 525 lines/frame

• Interlaced to reduce bandwidth– small interframe changes help

• Primary chromaticities:

x y zR 0.67 0.33 0.00G 0.21 0.71 0.08B 0.14 0.08 0.78W 0.310 0.316 0.374


• These yield 1.909 -0.985 0.058

RGB2XYZ = -0.532 1.997 -0.119-0.288 -0.028 0.902

Y=0.299R + 0.587G +0.114B (same as luminance channel for JPEG!)

= Y value of white point.

Cr = R-Y, Cb = B-Y with chromaticity: Cr: x=1.070, y=0; Cb: x=0.131 y=0;

y(C)=0 => Y(C)=0 => achromatic


• Signals are gamma corrected under assumption of dim surround viewing conditions (high saturation).

• Y, Cr, Cb signals (EY, Er, Eb) are sent per scan line; NTSC, SECAM, PAL do this in differing clever ways EY typically with twice the bandwidth of Er, Eb


• Y, Cr, Cb signals (EY, Er, Eb) are sent per scan line; NTSC, SECAM, PAL do this in differing clever ways.– EY with 4-10 x bandwidth of Er, Eb

– “Blue saving”

Digital HDTV

• 1987 - FCC seeks proposals for advanced tv– Broadcast industry wants analog, 2x lines of

NTSC for compatibility– Computer industry wanta digital

• 1993 (February) DHDTV demonstrated– in four incompatible systems

• 1993 (May) Grand Alliance formed

Digital HDTV

• 1996 (Dec 26) FCC accepts Grand Alliance Proposal of the Advanced Televisions Systems Committee ATSC

• 1999 first DHDTV broadcasts

http://www.atsc.org/

Digital HDTV

lines hpix aspect frames frame rateratio

720 1280 16/9 progressive 24, 30 or 60

1080 1920 16/9 interlaced 60

1080 1920 16/9 progressive 24, 30

• MPEG video compression

• Dolby AC-3 audio compression

Some gamuts

SWOP

ENCAD GA ink

Color naming

• A Computational model of Color Perception and Color Naming, Johann Lammens, Buffalo CS Ph.D. dissertation http://www.cs.buffalo.edu/pub/colornaming/diss/diss.html

• Cross language study of Berlin and Kay, 1969

• “Basic colors”

http://www.cs.buffalo.edu/pub/colornaming/diss/diss.html











Color naming

• “Basic colors”– Meaning not predicted from parts (e.g. blue,

yellow, but not bluish)– not subsumed in another color category, (e.g. red

but not crimson or scarlet)– can apply to any object (e.g. brown but not

blond)– highly meaningful across informants (red but not

chartruese)

Color naming

Num clrs colors2 White, black3 White, black, red4 White, black, red, green | yellow5 White, black, red, green, yellow6 White, black, red, green, yellow, blue7 White, black, red, green, yellow, blue, brown

8-11 White, black, red, green, yellow, blue, brown, purple | pink | orange | gray

• “Basic colors”– Vary with language

Color naming

• Berlin and Kay experiment:– Elicit all basic color terms from 329 Munsell

chips (40 equally spaced hues x 8 values plus 9 neutral hues

– Find best representative– Find boundaries of that

term

Color naming

• Berlin and Kay experiment:– Representative (“focus” constant across lang’s)

• Boundaries vary even across subjects and trials

• Lammens fits a linear+sigmoid model to each of R-B B-Y and Brightness data from macaque monkey LGN data of DeValois et. al.(1966) to get a color model. As usual this is two chromatic and one achromatic

Color naming

• To account for boundaries Lammens used standard statistical pattern recognition with the feature set determined by the coordinates in his color space defined by macaque LGN opponent responses.

• Has some theoretical but no(?) experimental justification for the model.

Canteen (Pantone 405 C)

Violet Quartz (Pantone 689 C)

Mecca Orange (Pantone 1675C)

Pantone Color Combo of the Month January 1999

RGB Models human visual system? Gives an absolute color description? Models color similarity? Linear...

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