Viewing Conditions and Chromatic Adaptationmajumder/vispercep/student08/colorappear… · Viewing...

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Viewing Conditions and Chromatic Adaptation

Visual Perception Spring 2008

Instructor: Prof. Aditi MajumderStudent: Hamed Pirsiavash

Agenda

Viewing fieldChromatic adaptationChromatic adaptation models

Linearnonlinear

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Viewing field

Self-luminous displaysCRT, LCD

Reflective mediaPainting

Viewing field

CIE illuminantD65, D50, A, F2, F8

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Viewing field

Modes of viewing

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Modes of viewing

Chromatic adaptation

Light adaptationTurning on the light in dark night

Dark adaptationEntering a dark movie theater


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Chromatic adaptationOriginal scene

Chromatic adaptationWithout chromatic adaptation (Tungsten illumination)

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Chromatic adaptationAfter chromatic adaptation


PhysiologyPupil Dilation/constrictionRod-cone transitionReceptor gain controlSubtractive mechanismsHigh-level adaptation

Spatial frequency adaptationMotion adaptation

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Chromatic adaptation models

Transformation from XYZ (tristimulus values) to LMS (cone responsitives)


From XYZ to LMS

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Von Kries model (1902)

Von Kries model

Von Kries model in matrix formIndependent channels

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Von Kries model

Transformation from one viewing condition to another

Von Kries model

Experimental results

Dark triangles: Von Kriesmodel

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Retinex theory (1971)Use spatial distribution of scene colorsColor appearance is

Surface reflectionNot the distribution of reflected light

Normalize the output of each sensor with average over the scene.

Nayatani’s model (1980)

NonlinearClose to MacAdam’smodel (1961) Noise term is added

Helps in low inllumination

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Guth’s model (1991)

Sigma is a noise term

Guth’s model (1991)

Comparison with Von Kriesmodel

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Fairchild’s model (1991)


Subscript n:Adapting stimulus

Subscript E:Equal energy illumination

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Inter channel correlationLater he removed this matrix


Transformation from one viewing condition to another

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Cat02 Model (2002)

A linear model used in CIE 2004

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Thanks for your attention

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Color Appearance Models

Arjun SatishMitsunobu Sugimoto

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Today's topic

Color Appearance ModelsCIELABThe Nayatani et al. ModelThe Hunt ModelThe RLAB Model

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Terminology recap

ColorHueBrightness/LightnessColorfulness/ChromaSaturation

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Color

Attribute of visual perception consisting of any combination of chromatic and achromatic content.Chromatic nameAchromatic nameothers

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Hue

Attribute of a visual sensation according to which an area appears to be similar to one of the perceived colorsOften refers red, green, blue, and yellow

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Brightness

Attribute of a visual sensation according to which an area appears to emit more or less light.Absolute level of the perception

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Lightness

The brightness of an area judged as a ratio to the brightness of a similarly illuminated area that appears to be whiteRelative amount of light reflected, or relative brightness normalized for changes in the illumination and view conditions

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Colorfulness

Attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic

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Chroma

Colorfulness of an area judged as a ratio of the brightness of a similarly illuminated area that appears whiteRelationship between colorfulness and chroma is similar to relationship between brightness and lightness

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Saturation

Colorfulness of an area judged as a ratio to its brightnessChroma – ratio to whiteSaturation – ratio to its brightness

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Definition of Color Appearance Model

so much description of colorsuch as: wavelength, cone response, tristimulus values, chromaticity coordinates, color spaces, …it is difficult to distinguish them correctlyWe need a model which makes them straightforward

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Definition of Color Appearance Model

CIE Technical Committee 1-34 (TC1-34)(Comission Internationale de l'Eclairage)They agreed on the following definition: A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue.CIELAB meets this criteria

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CIELABwhite

blue

green

black

red

yellow

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Construction of Color Appearance Models

All color appearance models start with CIE XYZ tristimulus valuesThe first process is the linear transformation from CIE XYZ tristimulusvalues to cone responsesso that we can more accurately model the physiological processes in the human visual system

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Calculating CIELAB Coordinate

To calculate CIELAB coordinates, one must begin with two sets of CIE XYZ tristimulus valuesStimulus XYZreference white XnYnZn

used to define the color "white"

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Then, add appropriate constantsL* = 116f(Y/Yn) – 16a* = 500[f(X/Xn) - f(Y/Yn)]b* = 200[f(Y/Yn) - f(Z/Zn)]

f(w) = w (if w > 0.008856)= 7.787(w)+16/116 (otherwise)

1/3

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L* = 116f(Y/Yn) – 16

L* is perceived lightness approximately ranging from 0.0 for black to 100.0 for white

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a* = 500[f(X/Xn) - f(Y/Yn)]b* = 200[f(Y/Yn) - f(Z/Zn)]

a* represents red-green chromaperceptionb* represents yellow-blue chromaperception

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a* = 500[f(X/Xn) - f(Y/Yn)]b* = 200[f(Y/Yn) - f(Z/Zn)]

They can be both negative and positive valueWhat does it mean if a value is 0.0?

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CIELAB color space

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Imagewhite

blue

green

black

red

yellow

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Chroma (magnitude)C*ab = [a* + b* ]Hue (angle)hab = tan (b*/a*)expressed in positive degrees starting at the positive a* axis and progressing in a counterclockwise direction

-1

2 2 1/2

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Example of CIELAB calculations

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Evaluation of CIELAB space

Plots of hue and chromafrom the Munsell Book of Color

Straight lines represent hue

Concentric circles represent chroma

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Further examinations using a system called CRT which is capable of achieving wider chroma than the Munsell Book of Color

Illustrated differences between observed and predicted results

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Unique hues

Red 24° (not 0°)

Yellow 90°

Green 162° (not 180°)

Blue 246° (not 270°)

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Summary of CIELAB (pros)

well-established, de facto international-standard color spacecapable of color appearance prediction

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Summary of CIELAB (cons)

limited ability to predict hueno luminance-level dependencyno background or surround dependencyand so on...

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Therefore...

CIELAB is used as a benchmark to measure more sophisticated models

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The Hunt Model

designed to predict a wide range of visual phenomenarequires an extensive list of input datacomplete modelcomplicated

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Input datachromaticity coordinates of the illuminant and the adapting fieldchromaticities and luminance factors of the background, proximal field, reference white, and test samplephotopic luminance LA and its color temparature Tchromatic surrounding induction factors Nc

brightness surrounding induction factors Nb

luminance of reference white Yw

luminance of background Yb

If some of these are not available, alternative values can be used

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Adaptation Model

In Hunt model, the cone responses are denoted ργβ rather than LMS

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Adaptation Model

There are many parameters need to be defined...

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The nonlinear response function fn

saturation

Threshold

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Adaptation Model

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Opponent-color Dimensions

Given the adapted cone signals, ρa, γa, and βa, one can calculate opponent-type visual responses very simply

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The achromatic post-adaptation signalAa is calculated by summing the cone responses with weights that represent their relative population in the retina

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The three color difference signals, C1, C2, and C3, represent all of the possible chromatic opponent signals that could be produced in the retina

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Others

Hue, saturation, brightness, lightness, chroma, and colorfulness also can be calculated by solving quite complicated equations…

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Summary of the Hunt model (pros)

seem to be able to do everything that anyone could ever want from a color appearance modelextremely flexiblecapable of making accurate predictionsfor a wide range of visual experiments

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Summary of the Hunt model (cons)

optimized parameter is required; otherwise, this model may perform extremely poorly, even worse than much simpler modelcomputationally expensivedifficult to implementRequires significant user knowledge to use consistently

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Color Appearance Models II

Arjun SatishMitsunobu Sugimoto

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Agenda

Nayatani et al Model. (1986) RLAB Model. (1990)

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Nayatani et al Model

Illumination engineering Color rendering properties of light

sources. Explanation of naturally occurring natural

phenomenon.

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Color Appearance Phenomenon

Stevens Effect Contrast Increase with luminance

Hunt Effect Colorfulness increases with luminance

Helson Judd Effect Change in hue depending on background

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Nayatani Model - Input Data

Background Luminance Factor, Y

o

Chromaticity Co-ordinates, xo and y

o.

Stimulus Luminance Factor, Y Chromaticity Co-ordinates, x and y.

Absolute luminance Eo

Normalizing Illuminance, Eor

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Nayatani Model - Starting Points

Use chromaticity coordinates.

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Use chromaticity coordinates. Convert them to 3 intermediate values.

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Use chromaticity coordinates. Convert them to 3 intermediate values.

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Adaptation Model

Calculate the cone responses for the adapting field

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Chromatic Adaptation Model

Adapted Cone Signals L

a, M

a, S

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Cone excitations L, M, S

Noise terms L

n, M

n, S

n

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Adaptation Model

Compute the exponents nonlinearities used in the chromatic adaptation model

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Adaptation Model

For the test stimulus,

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Opponent Color Dimensions

Use opponent theory to represent the cone response in achromatic and chromatic channels.

Single achromatic channel. Double chromatic channels.

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Achromatic Response

Considers only the middle and long wavelength cone response.

Logarithm -> model the nonlinearity of the human eye.

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Chromatic Channels

Tritanopic and Protanopic responses. Tritanopic

Red Green Response Protanopic

Blue Yellow Response

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Chromatic Channels

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Hue

Hue Angle Hue Quadrature Hue Composition

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Brightness

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Lightness

Calculated from the achromatic response alone.

Lp= Q + 50.

Black => Lp = 0;

White => Lp = 100;

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Pros and Cons

Pros 'Complete' model. Relatively simple.

Cons Changes in

background and surround

Not helpful for cross media applications.

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The RLAB Model

A color appearance model which would be suitable for most practical applications.

simple and easy to use. takes the positive aspects of CIELAB and

tries to overcome its drawbacks. application – cross media image

reproduction.

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Input Data

Tristimulus values of the test stimulus. Tristimulus values of the white point. Absolute luminance of a white object. Relative luminance of the surround.

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Adaptation Model

Cone Response

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Adaptation Model

Chromatic Adaptation

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Adaptation Model

Mapping the X,Y,Z to a reference viewing condition.

R = M-1 A-1, a constant.

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Opponent Color Dimensions

A 'better' and 'simplified' CIELAB.

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Exponents

= 1/2.3, for an average surround. = 1/2.9, for a dim surround. = 1/3.5, for a dark surround.

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Lightness

The RLAB Correlate of lightness is just LR !

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Hue

Hue Angle, hR = tan-1(bR/aR) Hue Composition, HR - same as before.

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Chroma and Saturation

CR = { (bR)2 + (aR)2 }1/2

SR = CR/ LR

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Pros and Cons

Pros Simple. Straightforward. Accurate.

Cons Can't be applied to

really large luminance ranges.

Does not explain Hunt, Stevens model.

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Thanks!

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