RGB

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RGB

• Models human visual system?
• Gives an absolute color description?
• Models color similarity?
• Linear model?
• Convenient for color displays?

2
RGB

• Models human visual system
• Gives an absolute color description
• Models color similarity
• Linear model
• Convenient for color displays

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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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Sources of colored light used in modern fireworks.

• Yellow Sodium D-line 589 nm
• Orange CaCl 591- 599 nm 603-608 nm
  
• Red SrCl 617-623 nm
  627-635 nm 640-646 nm
• Green BaCl 511-515 nm 524-528
  nm 530-533 nm
• Blue CuCl 403-456 nm,

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Lens
Retina
Cornea
Fovea
Pupil
Optic nerve
Iris
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Optic nerve
Light
Ganglion
Amacrine
Bipolar
Horizontal
Cone
Rod
Epithelium
Retinal cross section
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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

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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

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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.

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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.

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Cones

• L (long wave), M (medium), S (short)
• describes sensitivity curves.
• Red, Green, Blue is a misnomer. See
  spectral sensitivity.

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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.

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Opponent Models

• Three channels leave the retina
• Red-Green (L-MS L-(M-S))
• Yellow-Blue(LM-S)
• Achromatic (LMS)
• Note that chromatic channels can have negative
  response (inhibition). This is difficult to model
  with light.

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Adaptation

• Luminance adaptation allows greater sensitivity
  but over narrow ranges
• Chromatic adaptation supports color constancy by
  compensating for changes in illuminating spectra.

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100
Luminance
10
1.0
Contrast Sensitivity
Red-Green
0.1
Blue-Yellow
0.001
-1
0
1
2
Log Spatial Frequency (cpd)
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Weber Fraction

• DI/I c, DI perceived change
• log DI log I log c perceived change vs I
• log DI l log I a yields
• DI c Il power law
• Many perceptual responses follow power laws with
  llt1, i.e. compressive non-linearity

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Other non-linearities

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Color matching

• Grassman laws of linearity (r1 r2)(l) r1(l)
  r2(l) (kr)(l) k(r(l))
• Hence for any stimulus s(l) and response r(l),
  total response is integral of s(l) r(l), taken
  over all l or approximatelyS s(l)r(l)

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Surround light
Primary lights
Surround field
Bipartite white screen
Subject
Test light
Primary lights
Test light
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Color matching

• M(l) RR(l) GG(l) BB(l)
• Metamers possible
• good RGB functions are like cone response
• bad Cant match all visible lights with any
  triple of monochromatic lights. Need to add some
  of primaries to the matched light

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Surround light
Primary lights
Surround field
Bipartite white screen
Subject
Test light
Primary lights
Test light
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Color matching

• Solution XYZ basis functions

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Color matching

• Note Y is V(l)
• None of these are lights
• Euclidean distance in RGB and in XYZ is not
  perceptually useful.
• Nothing about color appearance

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CIE Lab

• 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

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CIE Lab, Luv

• Euclidean distance corresponds to judgements of
  color difference, especially lightness
• Somewhat realistic nonlinearities modeled

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• Lightness.m
• colorPatch.m - matlab image repn.
• umbColormatching.m

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Color Appearance

• Absolute
• Brighness
• Colorfulness

• Relative
• Lightness
• Chroma
• rel to white point
• colorfulness/brightness(white)
• Saturation
• rel to own brightness
• colorfulness/brightness

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Photoshop Calibration

• File-gtColor-gtRGB
• RGB space
• Gamma
• White point
• Primaries
• Reset to sRGB!!!

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Photoshop color picker

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

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light
yellow
b
red
green
a
blue
CIE Lab space
dark
dark
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IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
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xyz2displayrgb

• SPD of color r,g,b

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xyz2displayrgb

• SPD of color r,g,b
• phosphor

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values
• xyzxyzbarphosphorr,g,b

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values
• xyzxyzbarphosphorr,g,b
• r,g,b

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values
• xyzxyzbarphosphorr,g,b
• r,g,b
• inv(xyzbarphosphor)xyz

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values
• xyzxyzbarphosphorr,g,b
• r,g,b
• inv(xyzbarphosphor)xyz
• mon2XYZ

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xyz2displayrgb

• SPD of color r,g,b
• phosphorr,g,b
• XYZ tristimulus values
• xyzxyzbarphosphorr,g,b
• r,g,b
• inv(xyzbarphosphor) xyz
• xyz2displayrgb

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Viewing Conditions

• Illuminant matters. Table 7-1 shows DE using two
  different illuminants.
• DE lt 2.5 is typically deemed a match.
• On the midterm using chromaticities for Munsell
  principal hues, calculate DE for the hues with
  Wandell monitor whitepoint and D65

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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

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Adaptation

• Light adaptation - quick
• Dark adaptation - slow

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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

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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).

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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.

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Chromatic adaptation models

• three independent gain controls
• La kLL
• MakMM
• SakSS
• L L-cone response, La adapted response of L
  cones, etc

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Chromatic adaptation models

• 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.

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Chromatic adaptation models

• 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

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Chromatic adaptation models

• 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

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Non-linear chromatic adaptation models

• Nayatani adds noise and power law in brightness.
• La aL((LLn)/(L0Ln))bL etc.
• La adapted L cone response
• Ln noise signal L0 response to adapting
  field
• aL fit from a color constancy hypothesis

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Nayatani Color Appearance Model

• Model components
• Nonlinear chromatic adaptation
• One achromatic, two chromatic color opponent
  channels weighted by cone population ratios

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Nayatani Color Appearance Model

• Model outputs
• Brightness as linear function of adapted cone
  responses (which are non-linear!)
• Lighness achromatic channel origin translated to
  black0, white 100
• Brightness of ideal white (perfect reflector)
• Hue angle (from the chromatic channels)

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Nayatani Color Appearance Model

• 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 saturationlightness
• Colorfullness Chromabrightness of ideal white.

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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

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Nayatani Color Appearance Model Weaknesses

• Doesnt 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

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Color Appearance

• Absolute
• Brighness
• Colorfulness

• Relative
• Lightness
• Chroma
• rel to white point
• colorfulness/brightness(white)
• Saturation
• rel to own brightness
• colorfulness/brightness

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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

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Hunt Color Appearance Model

• Inputs
• absolute luminance of
• reference white
• adapting field
• scotopic luminance data
• parameters for chromatic and brightness induction

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Hunt Color Appearance Model

• Properties
• Non-linear responses
• Models incomplete chromatic adaptation
• Chromatic adaptation constants depend on
  luminance
• Models saturation
• Models brightness, lightness, chroma and
  colorfulness

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Hunt Color Appearance Model

• 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

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Testing Color Appearance Models

• Qualitative tests
• Corresponding colors data (colors which appear
  the same when viewed under different conditions)
• Magnitude estimation tests
• Psychophysics

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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.

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Testing Color Appearance Models- Qualitative Tests

• 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.

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Testing Color Appearance Models- Qualitative Tests

• Adjust parameters to predict constancies in
  standard color order systems (e.g. constant
  Lab 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.

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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

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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

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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

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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)

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MacAdam Ellipses

• JND of chromaticity
• Bipartite equiluminant color matching to a given
  stimulus.
• Depends on chromaticity both in magnitude and
  direction.

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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

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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?

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MacAdam Ellipses

• JND of chromaticity
• Extension to varying luminence ellipsoids in XYZ
  space which project appropriately for fixed
  luminence

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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/freean
  dclear.htm and http//www.ductus.com/cleartype/cle
  artype.html

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Complementary Colors

• Colors which sum to white point are called
  complementary colors
• ac1bc2 wp
• Some monochromatic colors have complements,
  others dont. See ComplementaryColors.m
• Complements may be out of gamut. See Photoshop.

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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)

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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.

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Printing

• Inks are subtractive
• Cyan (white - red)
• Magenta (white - green)
• Yellow (white - blue)
• In practice inks are opaque, so cant do mixing
  like oil paints.
• May use black ink on economic and physical grounds

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Halftoning

• The problem with ink its 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.

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Halftoning references

• A commercial but good set of tutorials
• Digital Halftoning, by Robert Ulichney, MIT
  Press, 1987
• Stochastic halftoning

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Color halftoning

• Needs screens at different angles to avoid moire
• Needs differential color weighting due to
  nonlinear visual color response and spatial
  frequency dependencies.

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Device Independence

• Calibration to standard space
• typically CIE XYZ
• Coordinate transforms through standard space
• Gamut mapping

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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.

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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
  XYZf(device1, device2, device3) this
  approximates interpolating to tangent.

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XYZ to Device

• Invert function XYZf(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)

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XYZ to CMY

• Invert function XYZf(c,m,y)
• Given XYZx,y,z want to find CMYc,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.

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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 UX(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.

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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

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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

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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.

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Gamuts
CRT
Printer
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Gamut Mapping

• First try map black points and fill destination
  gamut.

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device gamut
image gamut
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device gamut
translate Bito Bd
image gamut
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device gamut
translate Bito Bd
image gamut
scale by csf
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device gamut
translate Bito Bd
image gamut
scale by csf
rotate
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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, ...

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Xd Bd csf R (Xi - Bi) bs (Wd - Bd)
shift and scale alongdestination gray
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Fig 14a, bsgt0, csf small, image gamut maps
entirelyinto printer gamut, but contrast is low.
Fig 14b, bs0, csf large, more contrast, more
colors inside printer gamut, butalso more
outside.
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Saturation control

• Umbrella transformation
• Rs Gs Bs monitor whitepoint
• Rn Gn Bn new RGB coordinates such that Rs
  Gs Bs Rn Gn Bnand Rn Gn Bn maps
  inside destination gamut
• First map R RsG GsB Bs to R RnG GnB Bn
• Then map into printer coordinates
• Makes minor hue changes, but relative colors
  preserved. Achromatic remain achromatic.

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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

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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.

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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.

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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

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ICC Color Profiles

• International Color Consortium http//www.color.or
  g.
• ICC Profile
• device description text
• characterization data
• calibration data
• invertible transforms to a fixed virtual color
  space, the Profile Connection Space (PCS)

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Profile Connection Space

• Presently only two PCSs CIELAB and CIEXYZ
• Both specified with D50 white point
• Devicelt--gtPCS must account for viewing
  conditions, gamut mapping and tone (e.g. gamma)
  mapping.

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Gamut mapping, tone control, etc
Viewing-conditionindependent space
Input imageand device
Chromatic adaptation and color appearance models
input devicecolorimetriccharacterization
DVI color space(PCS)
DVI color cpace
DVI color space (e.g. XYZ)
Chromatic adaptation and color appearance models
output devicecolorimetriccharacterization
Chromatic adaptation and color appearance models
Output image and device
Viewing-conditionindependent space
Gamut mapping, tone control, etc
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ICC Profiles

• Device profiles
• Colorspace profiles
• data conversion
• Device Link profile
• concatenated D1-gtPCS-gtD2
• Abstract profile
• generic for private purposes, e.g. special effects

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ICC Profiles

• Named color profile
• Allows data described in Pantone system (and
  others?) to map to other devices, e.g. view.
• Supported in Photoshop

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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)

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ICC Profile Data Tags

• 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)

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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

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ICC Profile Data Tags

• 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

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ICC Profile Data Tags

• 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.

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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

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JPEG DCT Quantization

• FDCT of 8x8 blocks.
• Order in increasing spatial frequency (zigzag)
• Low frequencies have more shape information, get
  finer quantization.
• Highs 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)

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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.

139
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 015, j 110, and s0,0EOB s15,0 ZRL
• symbol-2 Huffman encoding of dct amplitude
• Finally, these 162 symbols are Huffman encoded.

140
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
  1/21/31/21/3 1/3
• More shape info in achromatic than chromatic
  components. (Color vision poor at localization).

141
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?

142
Huffman Encoding
143
1110101101100Traverse from root to leaf, then
repeat 11 1010 11 01 100 s3 s5 s3 s2 s4
Huffman Encoding
144
Charge Coupled Device (CCD)
lt 10mm x 10mm
Silicon cells emit electrons when light falls on
it
145
(No Transcript)
146
Filters over cells
More green than red, blue
Y0.299R 0.587G 0.114B
(For color tv and?)
147
CCD Cameras

• Good links
• http//denton.chem.arizona.edu/ccd/
• Some device specs
• http//www.MASDKODAK.com/

148
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)

149
NTSC Analog Color TV

• 525 lines/frame
• Interlaced to reduce bandwidth
• small interframe changes help
• Primary chromaticities

150
NTSC Analog Color TV

• These yield
• 1.909 -0.985 0.058RGB2XYZ -0.532
  1.997 -0.119 -0.288 -0.028 0.902
• Y0.299R 0.587G 0.114B (same as
  luminance channel for JPEG!) Y value of white
  point.
• Cr R-Y, Cb B-Y with chromaticity Cr
  x1.070, y0 Cb x0.131 y0
• y(C)0 gt Y(C)0 gt achromatic

151
NTSC Analog Color TV

• 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

152
NTSC Analog Color TV

• 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

153
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

154
Digital HDTV

• 1996 (Dec 26) FCC accepts Grand Alliance Proposal
  of the Advanced Televisions Systems Committee
  ATSC
• 1999 first DHDTV broadcasts

155
Digital HDTV

• lines hpix aspect frames frame rate ratio
  
• 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

156
Some gamuts
157
Color naming

• A Computational model of Color Perception and
  Color Naming, Johann Lammens, Buffalo CS Ph.D.
  dissertation http//www.cs.buffalo.edu/pub/colorna
  ming/diss/diss.html
• Cross language study of Berlin and Kay, 1969
• Basic colors

158
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)

159
Color naming

• Basic colors
• Vary with language

160
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

161
Color naming

• Berlin and Kay experiment
• Representative (focus constant across langs)
• Boundaries vary even across subjects and trials
• Lammens fits a linearsigmoid 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

162
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.

163
Pantone Color Combo of the Month January 1999
That's all for today