Introduction to Color Appearance Models Outline

Introduction to Color Appearance Models

Arto Kaarna

Lappeenranta University of Technology Department of Information Technology P.O. Box 20 FIN-53851 Lappeenranta, Finland [email protected]

Basics of CAM … 1/81

Outline

1. Definitions 3 2. Color Appearance Phenomena 15 3. Chromatic Adaptation 44 4. Color appearance models 59 5. CIECAM02 65 Literature 80

Basics of CAM … 2/81

1 Definitions

Needed for uniform and universal description Precise for mathematical manipulation International Lighting Vocabulary (CIE, 1987) Within color science Color Hue Brightness Lightness Colorfulness Chroma Saturation Unrelated and Related Colors

Basics of CAM … 3/81

Definitions

Color Visual perception of chromatic or achromatic content •Yellow, blue, red, etc •White, gray, black, etc. •Dark, dim, bright, light, etc. NOTE: perceived color depends on the spectral distribution of the stimulus, size, shape, structure, and surround •Also state of adaptation of the observer Both physical, physiological, psyckological and cognitive variables New attempt: visual stimulus without spatial or temporal variations More detailed definition needed using more parameters for numeric expression

Basics of CAM … 4/81

2 Definitions

Hue Visual sensation similar to one of the perceived colors: red, yellow, green, and blue or their combination Chromatic color: posessing a hue, achromatic no hue

Munsell book of color No hue with zero value Achromatic colors

Basics of CAM … 5/81

Definitions

Brightness A visual sensation according to which an area emits more or less light An absolute level of perception Lightness The brightness of an area judged relative to the brightness of a similarly illuminated area that is white or highly transmitting, thus a relative value For example: A page from a book Office •Brightness: some value •Lightness: high value Sunny day: •Higher brightness value •Roughly the same lightness as in office

Basics of CAM … 6/81

3 Definitions

Basics of CAM … 7/81

Definitions

Colorfulness A visual sensation in which an area appears to be more or less chromatic Defines the intensitivity of the hue of a given stimulus Chroma Colorfulness of an area as a proportion of the brightness of a similarly illuminated area of white or high transmittance

The third dimension of 3D color vision (hue+brightness/lightness+colorfulness/chroma) Colorfulness and chroma in similar relationship as brightness and lightness Zero colorfulness or chroma: achromatic colors

Basics of CAM … 8/81

4 Definitions

Increase in lightness

Increase in colorfulness

Basics of CAM … 9/81

Definitions

Saturation Colorfulness of an area in proportion to its own brightness Relative colorfulness Traffic lights at night

Increase in saturation

Increase in lightness

Increase in colorfulness

Basics of CAM … 10/81

5 Definitions

Unrelated Colors Seen in isolation of other colors Many visual experiments with unrelated colors Related Colors Seen in relation to other colors Color appearance applications deal with related colors

Colors like gray and brown Gray has lower lightness than white Brown is an orange color with low lightness Gray/brown light in dark environment?

Basics of CAM … 11/81

Definitions

Unrelated Colors Perceptual attributes are hue, brightness, colorfulness, and saturation No attributes related to similarly illuminated white Related colors Perceptual attributes are hue, brightness, lightness, colorfulness, chroma, and saturation

Test: White/orange illuminant in dark environment surrounded with an increasing white illuminant? White/orange illuminant in highly illuminated environment? What is the color of the white/orange illuminant?

Basics of CAM … 12/81

6 Definitions

Finally we get the equations Colorfulness Colorfulness Chroma = Saturation = Brightness(white) Brightness Brightness Lightness = Brightness(white) Chroma Saturation = Lightness Colorfulness Brightness(white) = × Brightness(white) Brightness Five perceptual dimensions for color appearance Brightness, lightness, colorfulness, chroma, hue, saturation is redundant

Basics of CAM … 13/81

Definitions

Brightness-colorfulness vs. lightness-chroma Example Object in sunny day: hue(red), high brightness, lightness, colorfulness, chroma Object in office: hue, lightness and chroma same, brightness and colorfulness lower

Basics of CAM … 14/81

7 Color Appearance Phenomena

Relationships in viewing conditions and color appearance, examples Simultaneous Contrast Josef Albers Complex Spatial Structures Hunt Effect Stevens Effect Bartleson-Breneman Surround Effect

Basics of CAM … 15/81

Color Appearance Phenomena

Equal (XYZ)1 = (XYZ)2 appear similar Similar viewing conditions for both samples: surrounds, backgrouds, size, shape, surface, illumination, etc. Differences can be computed from (XYZ) pairs

Basics of CAM … 16/81

8 Color Appearance Phenomena

If two samples with (XYZ)1 = (XYZ)2 look different then there is a change in viewing conditions

Basics of CAM … 17/81

Color Appearance Phenomena

Basics of CAM … 18/81

9 Color Appearance Phenomena

Cognition through vision

Basics of CAM … 19/81

Hermann Grid

Basics of CAM … 20/81

10 Scintillation Effect

Basics of CAM … 21/81

Basics of CAM … 22/81

11 Basics of CAM … 23/81

Simultaneous Contrast

The background influences the apparent color of the sample

Bg change Sample change Darker Lighter Lighter Darker Red Green Green Red Yellow Blue Blue Yellow

Basics of CAM … 24/81

12 Simultaneous Contrast

Constant gray-level of a sample

Basics of CAM … 25/81

Simultaneous Contrast

Constant color of a sample Robertson (1996): yellow stripe ĺ square gets bluer

Basics of CAM … 26/81

13 Josef Albers

Studies on simulteneous contrast

Josef Albers: Interaction of Color, 1963

Basics of CAM … 27/81

Crispening

Similar gray-level at different backgrounds Differences between squares change with different backgrounds

Basics of CAM … 28/81

14 Spreading

High-frequency sample close to background Left: more greenish, right: more reddish

Basics of CAM … 29/81

Bezold-Brucke

Hue cannot be determined directly from wavelength Hue shift: hue changes with luminance Largest change in red-yellow area 650nm ĺ 620 nm when luminance is 1/10th of the original

Basics of CAM … 30/81

15 Abney effect

Mixing of monocromatic light with white light does not preserve constant hue

Y 0.8

0.6 Contours at constant hue Constant hue produced by various wavelengths 0.4

0.2

0.2 0.4 0.6 0.8 x

Basics of CAM … 31/81

Helmholtz-Kohlrausch

Brightness depends on luminance and chromaticity Correction factor: F = 0.256 - 0.184 y - 2.527 xy + 4.656 x 3 y + 4.657 xy 4

Equal brightness: log( L1 ) + F1 = log( L 2 ) + F2 Y 0.8

0.6 1.2 Contours at constant brightness- 1.3 1.1 to-luminance ratio

1.4 Constant luminance ĺ perceived 0.4 1.0 brightness increases when

1.5 stimulus becomes more chromatic 0.2

0.2 0.4 0.6 0.8 x

Basics of CAM … 32/81

16 Hunt Effect

When luminance level changes the color appearance of objects changes Objects vivid and contrasty in summer afternoon Subdued in dusk

Y 10000 0.8

1000 For a constant chromaticity, perceived colorfulness increases 100 0.6 with luminance

10 With luminance increase, a lower colorimetric purity is needed to 0.4 1 match with the reference stimulus 10000 1000 100 10 1 0.2

0.2 0.4 0.6 0.8 x Basics of CAM … 33/81

Hunt Effect

At low level of illumination, the colorfulness is low In brighter viewing environment, the elements will be more colorful Absolute luminance level is needed in an appearance model

Basics of CAM … 34/81

17 Stevens Effect

Contrast increases with luminance: perceived contrast increases in with increasing luminance

Lower luminance Luminance level increases: light colors become lighter, dark colors become darker

Higher luminance 0.1 Relative0.1 Brightness 1.0

0.1 Relative luminance 1.0

Basics of CAM … 35/81

Stevens Effect

Black-and-white image: white is more white and black is more black in higher level luminance conditions

Basics of CAM … 36/81

18 Bartleson-Breneman

Complex stimuli while varying luminance level and surround Percieved contrast of images increased when the surround was changed from dark to dim to light 1.0 Dark Dark background makes dark colors darker without affecting to light colors Dark background lowers and light background adds contrast Average 0.0 Lightness 0.0

0.0 Relative luminance 1.0 Basics of CAM … 37/81

Bartleson-Breneman

Basics of CAM … 38/81

19 Spatial Interactions

Spatial variables influence appearance Surround effects

Basics of CAM … 39/81

Spatial Interactions

Basics of CAM … 40/81

20 Dale Purves, Brown

Brown patch under changing illumination See demonstration: http://www.purveslab.net/main/

Basics of CAM … 41/81

Viewing Conditions

Color appearance depends on the simulus itself and other stimuli nearby Spatial Temporal Laboratory vs. practical applications and measurements

Stimulus Proximal field Background Surround

Basics of CAM … 42/81

21 Viewing Conditions

Stimulus The area for which the color appearance is desired 2 degree angle (CIE 1931) Color in the angle vs. color of the object Proximal Field Immediate field around stimuli in all directions Local contrast effects: crispening, spreading Nearest pixels in digital images In many cases same as background Background About 10 degree field For simultaneous contrast definition Surround Area outside background

Basics of CAM … 43/81

Chromatic Adaptation

Light adaptation Decrease in visual sensitivity upon increase in overall illumination level Switch the lights on when you wake up Dark adaptation Increase in visual sensitivity upon decrease in illumination level From sunlight to dark room

Chromatic adaptation Adaptation to changes in chromatic values of the illumination Most important property of the human eye as part of the color appearance model

Basics of CAM … 44/81

22 Chromatic Adaptation daylight

Chromatic Adaptation

3000K (as seen in daylight without chromatic adaptation)

23 Chromatic Adaptation

9000K (as seen in daylight without chromatic adaptation)

Chromatic Adaptation

Blue filter over the image

24 Chromatic Adaptation

Blue filter over one sample

Chromatic Adaptation

Afterimage by local retinal adaptation

Basics of CAM … 50/81

25 Chromatic Adaptation

Basics of CAM … 51/81

Chromatic Adaptation

Corresponding colors

Two stimuli (XYZ)1 , (XYZ)2 that match in color appearance under different viewing conditions Obtained through asymmetric matching •Left and right halves of the retina •Haploscopic matching: Left eye, right eye have they own viewing conditions

Corresponding colors in D65 and A

Fairchild, 1998

Basics of CAM … 52/81

26 Model for Chromatic Adaptation

Model for chromatic adaptation Allows prediction of the corresponding colors Models tested with corresponding color sets

Computation of the three cone signals La Ma Sa

X1 Y1 Z1 L1 M1 S1 La Ma Sa L2 M2 S2 X2 Y2 Z2

3*3 VC1 VC2 3*3

CAT is an extension to tristimulus colorimetry Changes from illumination can be predicted

Basics of CAM … 53/81

Model for Chromatic Adaptation

3*3 from CIE to LMS using a linear transform

Maureen C. Stone, 2005 é L ù é 0.400 0.708 - 0.081ùéX ù êM ú = ê- 0.226 1.165 0.046 úêY ú ê ú ê úê ú ëê S ûú ëê 0.000 0.000 0.918 ûúëêZ ûú

Basics of CAM … 54/81

27 Model for Chromatic Adaptation von Kries model for chromatic adaptation (1902) Cones operate (adapt or fatigued) independent of the others

La = k L L k L = 1/ Lmax kL = 1/ Lwhite k = 1/ M M a = kM M k M = 1/ M max M white k = 1/ S Sa = kS S k S = 1/ Smax S white

Short wavelengths -> scaling of Sa to a lower value Inverse transform

L2 = (L1 / Lmax1 )Lmax 2

M 2 = (M 1 / M max1 )M max 2

S 2 = (S1 / S max1 )Smax 2

Basics of CAM … 55/81

Model for Chromatic Adaptation

Model by von Kries

Breneman corresponding colors; open triangles von Kries model: closed triangles Daylight vs. incandescent light

Fairchild, 1998

Basics of CAM … 56/81

28 Model for Chromatic Adaptation

Model by Nayatani ȕ æ L + L ö L Hunt, Stevens effects ç n ÷ La = a L ç ÷ è L0 + Ln ø ȕ æ M + M ö M M a ç n ÷ a = M ç ÷ è M 0 + M n ø

ȕ æ S + S ö S ç n ÷ S a = a S ç ÷ è S 0 + S n ø Open symbols: measurements Closed symbols: predictions by the model

Fairchild, 1998

Basics of CAM … 57/81

Model for Chromatic Adaptation

CAT02 (CIE 2004, CIE TC 8-01) Linear model

éX 2 ù éRadapt2 0 0 ù é1 Radapt1 0 0 ù éX1 ù ê ú ê ú ê Y ú = M -1 0 G 0 0 1 G 0 M ê Y ú ê 2 ú CAT 02 ê adapt2 ú ê adapt1 ú CAT 02 ê 1 ú ê ú ê ú ê ú ê ú ë Z2 û ë 0 0 Badapt2 û ë 0 0 1 Badapt1 û ë Z1 û é 0.7328 0.4296 - 0.1624ù M = ê- 0.7036 1.6975 0.0061 ú CAT 02 ê ú ëê 0.0030 0.0136 0.9834 ûú

XYZ to RGB, adaptation VC1, adaptation VC2, RGB to XYZ

Various transform matrices have been developed

Basics of CAM … 58/81

29 Color Appearance Models

History in CAM Hunt 1994, 1996 (Hunt & Luo) •Mother of all CAM Nayatani 1997 (Osaka, Japan) •Continuation of earlier work on chromatic adaptation RLAB 1996 (RIT, USA) •Background in CIECAM, extension to CAT LLAB 1996 (CII, UK) •Extension to RLAB CIECAM97 (Hunt & Luo) •Vienna, 1996: 12 principles for CAM CIECAM02 (Moroney, Fairchild, Hunt, Li, Luo, Newman) More details from literature e.g.: Mark Fairchild: CAM, 2nd Edition, 2005

Basics of CAM … 59/81

Color Appearance Models

12 principles Comprehensive for many applications Wide range of stimulus values, adapting values, viewing conditions CIE xyz spectral sensitives Large range of adaption between complete and none Predictions to all color parameters Also reverse mode Not too complicated, also simpler version for specific applications Model gives best results Works also with unrelated colors CIE TC 1-34 (1992) (Testing CAM) What to test? Evaluating models Recommending models for general use, finally: recommend one model

Basics of CAM … 60/81

30 Color Appearance Models

Previous models concern only chromatic changes in illumination CAM, also accounting for Level of illumination Proximal field, surround, background CAM uses absolute levels (brightness, colorfulness, hue) and relative levels (lightness, chroma, saturation, hue).

CAM: a model of color vision capable of predicting color appearance under different viewing conditions Chromatic adaptation Predictors at least to lightness, chroma, and hue

Basics of CAM … 61/81

Color Appearance Models

CIELAB as CAM

XYZ as a starting point, XnYnZn as reference white Normally called a uniform color space •color differences equal perception •Various color difference formulas ǻE developed Responses

L*: light to dark L* = 116 f (Y /Yn ) -16

a*: green-red a* = 500[ f ( X / X n ) - f (Y / Yn )]

b*: blue-yellow b* = 200[ f (Y / Yn ) - f (Z / Z n )] CIELAB + ì (w)1/ 3 w > 0.008856 f (w) = í Models chromatic adaptation î7.787(w) + 16 /116 w £ 0.008856 Lightness, chroma, hue 2 2 Color differences C ab * = (a * +b * ) -1 Works well in near-daylight hab * = tan (b * / a*)

Basics of CAM … 62/81

31 Color Appearance Models

CIELAB – No background, surround, luminance, cognition Cannot predict brightness, colorfulness Wrong von Kries model Constant hue-predictions could be better

Constant percieved hue

Basics of CAM … 63/81

Color Appearance Models

Wrong von Kries model

La k L 0 0 L X a kL 0 0 X

M a = 0 k M 0 M M Ya = 0 k M 0 M Y

S a 0 0 k S S Z a 0 0 k S Z

é 0.390 0.689 - 0.079ù ê ú M = ê- 0.230 1.183 0.046 ú ëê 0 0 1.000 ûú

X a 0.74k L + 0.26k M 1.32k L - 1.32k M - 0.15k L - 0.05k M + 0.20k S X

Ya = 0.14k L - 0.14k M 0.26k L + 0.74k M - 0.03k L + 0.03k M Y

Z a 0 0 k S Z

Basics of CAM … 64/81

32 Color Appearance Models

CIELAB CIELUV Visual data: open symbols CIELAB/CIELUV prediction: filled triangles Results better with the original von Kries model

Basics of CAM … 65/81

CIECAM02

Simplified and improved from CIECAM97 CIE TC8-01: Colour Appearance Models for Colour Management Systems Brightness (Q)

Lightness (J) X Y Z CAM Colourfulness (M) Chroma(C)

Saturation(s)

Hueangle(h) XwYwZw L Y D a b Hue composition (H)

Basics of CAM … 66/81

33 CIECAM02, 1/7

Surround relative luminance Average for reflection prints Dim for CRTs Dark for projected transparencies

Viewing Condition c Nc F Average surround 0.69 1.0 1.0 Dim surround 0.59 0.9 0.9 Dark Surround 0.525 0.8 0.8

Interpolation possible: estimate c, interpolate others

Basics of CAM … 67/81

CIECAM02, 2/7

Chromatic adaptation von Kries type linear model R X é 0.7328 0.4296 - 0.1624 ù ê ú G = M CAT02 Y M = - 0.7036 1.6975 0.0061 CAT02 ê ú B Z ëê 0.0030 0.0136 0.9834 ûú Degree of adaptation D as a function of adapting luminance LA and surround F

æ -( L +42 ö é æ 1 ö ç A ÷ ù D = 0 no adaptation D = F ê1 - ç ÷e è 92 ø ú 3.6 D = 1 complete adaptation (discounti ng the illuminant ) ëê è ø ûú Adapted tristimulus responses are then R = (100 D / R ) + (1 - D) R C [ W ] YW instead of 100

GC = [(100 D / GW ) + (1 - D)]G

BC = [(100 D / BW ) + (1 - D)]B Basics of CAM … 68/81

34 CIECAM02, 3/7

Viewing-condition-dependent components

Luminance-level factor FL

Induction factors Nbb and Ncb Base exponential nonlinearity z

k = 1/(5L A + 1) 4 4 2 1 / 3 FL = 0.2k (5L A ) + 0.1(1 - k ) (5L A ) Y n = b YW 0.2 N bb = N cb = 0.725(1/ n) z = 1.48 + n

Basics of CAM … 69/81

CIECAM02, 4/7

To fundamentals that more closely represent cone responsivities

R' RC -1 G' = M HPE M CAT 02 GC

B' BC 0.38971 0.68898 - 0.07868

M HPE = - 0.22981 1.18340 0.04641 0.00000 0.00000 1.00000

1.096124 - 0.278869 0.182745 -1 M CAT 02 = 0.454369 0.473533 0.072098 - 0.009628 - 0.005698 1.015326

Basics of CAM … 70/81

35 CIECAM02, 5/7

Primaries for further computation

0.42 ' 400(FL R' /100 ) Ra = 0.42 + 0.1 27.13 + (FL R' /100 )

0.42 ' 400(FL G' /100) G a = 0.42 + 0.1 27.13 + (FL G' /100)

0.42 ' 400(FL B' /100) Ba = 0.42 + 0.1 27.13 + (FL B'/100) Opponent colors

' ' ' a = R a - 12G a /11 + Ba /11

' ' ' b = (1/ 9)(R a + G a - 2Ba )

Basics of CAM … 71/81

CIECAM02, 6/7

Hue similarly to CIELAB h = tan -1 (b / a)

Hue quadrature (in range 0…400) 0 (red), 100 (yellow), 200 (green), 300 (blue), 400 (red)

é æ p ö ù 100(h - hi ) / ei et = 1/ 4êcosçh + 2÷ + 3.8ú H = Hi + ë è 180 ø û (h - hi ) / ei + (hi+1 - h) / ei+1 Lightness

' ' ' cz A = [2Ra + G a + (1/ 20)Ba - 0.305 ]N bb J = 100 (A / AW )

Brightness

0.25 Q = (4 / c) J /100 (AW + 4)FL

Basics of CAM … 72/81

36 CIECAM02, 7/7

Chroma

2 2 (50000/13)N N e a + b 0.9 n 0.73 c cb t C = t J /100 1.64 - 0.29 t = ' ' ' ( ) Ra + Ga + (21/ 20)Ba

Colorfulness

0.25 M = CF L Saturation s = 100 M / Q

Basics of CAM … 73/81

CIECAM02, inverse model

Starting point JCh 1. Calculate t from C and J.

2. Calculate et from h.

3. Calculate A from AW and J.

4. Calculate a and b from t, et, h, and A.

5. Calculate R’a, G’a, and B’a from A, a, and b. 6. Use inverse nonlinearity to compute R’, G’, and B’.

7. Convert to Rc, Gc, Bc via linear transform. 8. Invert CAT to compute RGB and then XYZ.

Conclusion: CIECAM02 is the CAM

Basics of CAM … 74/81

37 CIECAM02

Effects predicted Required components of color (7) Chromatic adaptation Hunt effect Stevens effect Bartleson-Breneman effect Discounting the illuminant Color difference ?

Basics of CAM … 75/81

CIECAM02

Color differences for CIECAM02 Not originally defined New proposals like Luo, 2006

' 2 2 2 DE = (DJ ' / K L ) + Da' +Db'

' (1 + 100c1 )J ' J = M = (1/ c2 ) ln(1+ c 2 M ) 1 + c1 J a'= M ' cos( h) b'= M 'sin( h)

Data CAM02-LCD CAM02-SCD CAM02-UCS

KL 0.77 1.24 1.00

c1 0.007 0.007 0.007

c2 0.0053 0.0363 0.0228

Basics of CAM … 76/81

38 CIECAM02

Basics of CAM … 77/81

CIECAM02

Basics of CAM … 78/81

39 Basics of CAM … 79/81

CAM, literature

Berns Roy S. Billmeyer and Saltzman’sprinciples of color technology, 3rd ed. New York, Wiley, cop. 2000, 247 s.ISBN:0-471-19459- X. CIE (2004), CIE TC8-01 Technical Report, A Colour Appearance Model for Color Management Systems: CIECAM02, CIE Pub. 159 (2004). Fairchild Mark D. (1998) Color appearance models. Reading (MA): Addison-Wesley 417 s. [First edition 1997] ISBN 0-201-63464-3 Corrections: http://www.cis.rit.edu/fairchild/CAM.html. Fairchild Mark D. (2005) Color appearance models 2nd ed. Chichester, Wiley, 385 s. ISBN: 0-470-01216-1 Corrections: http://www.cis.rit.edu/fairchild/CAM.html. Hunt R. W. G. (1991) Measuring Colour - 2nd ed. New York: Ellis Horwood, 313s. ISBN 0-13-567686-X.

Basics of CAM … 80/81

40 CAM, literature

Luo M. Ronnier, Cui Guihua, Li Changjun (2006) Uniform colour spaces based on CIECAM02 colour appearance model. Color Research & Application,Vol. 31, Issue 4, August 2006, s. 320-330. Luo M. Ronnier (2006) Colour Difference Formulae: Past, Present and Future. (abstract), ISCC/CIE Expert Symposium, Ottawa, Ontario, 29.05.2006, http://www.iscc.org/jubilee2006/abstracts.html. Nayatani Yoshinobu (2006) Development of chromatic adaptation transforms and concept for their classification. Color Research & Application Vol. 31, Issue 3, June 2006, s. 205-217. Wyszecki, Günther, W.S. Stiles (1982) Color science : concepts and methods, quantitative data and formulae. Second Edition. New York : Wiley, cop., (Classics Library Edition published 2000) 950 s. ISBN: 0-471-02106-3 (Classics Library Edition).

Basics of CAM … 81/81