1. Color Appearance Models The Nayatani et al. Model The Hunt 91 and 94 Model The RLAB Model Iris Zhao April 21, 2004

2. Background Color-appearance terminology Color-appearance terminology Brightness Vs. lightness Brightness Vs. lightness Colorfulness Vs. Chroma Colorfulness Vs. Chroma Color-appearance phenomena Color-appearance phenomena Hunt Effect (Colorfulness increase with luminance) Stevens Effect (Contrast increase with luminance) Helson-Judd Effect (hue of nonselective samples) Helson-Judd Effect (hue of nonselective samples) Helmholtz-Kohlrausch Effect (Brightness depends on luminance and chromaticity) Helmholtz-Kohlrausch Effect (Brightness depends on luminance and chromaticity) Bezold-Brϋcke hue shift Bezold-Brϋcke hue shift Chromatic adaptation and chromatic-adaptation models Chromatic adaptation and chromatic-adaptation models von Kries Model von Kries Model Nayatani’s Model Nayatani’s Model Fairchild’s Model Fairchild’s Model

3. Color-appearance model and chromatic-adaptation transform Y. Nayatani, etc The Nayatani et al. Model

4. Outline Extension of a nonlinear color-appearance model to white and light-gray background. Extension of a nonlinear color-appearance model to white and light-gray background. Two chromatic-adaptation transforms : lightness-chroma (Q-C) transform and brightness-colorfulness (B-M) transform. Two chromatic-adaptation transforms : lightness-chroma (Q-C) transform and brightness-colorfulness (B-M) transform. The usefulness of a combination of a color-appearance model and its corresponding chromatic transform. The usefulness of a combination of a color-appearance model and its corresponding chromatic transform. The importance of B-M transform. The importance of B-M transform.

5. Variables The input data for the model include as follows: The luminance factor of the background, Y 0 The luminance factor of the background, Y 0 Chromaticity coordinates of illuminant, x 0, y 0 Chromaticity coordinates of illuminant, x 0, y 0 The test stimulus, x, y, Y The test stimulus, x, y, Y The absolute luminance of the stimulus and adapting field, E 0 The absolute luminance of the stimulus and adapting field, E 0 The normalizing illuminance, E or The normalizing illuminance, E or Cone response for adapting field, (xi), (eta) and (zeta) Cone response for adapting field, (xi), (eta) and (zeta) Cone responses for adapting field in terms of the absolute luminance level, R 0, G 0, B 0 Cone responses for adapting field in terms of the absolute luminance level, R 0, G 0, B 0 Cone responses for the test stimulus, R, G, B Cone responses for the test stimulus, R, G, B Opponent-color dimensions, Q, T, P Opponent-color dimensions, Q, T, P Brightness for a stimulus and an ideal white, B r and B rw Brightness for a stimulus and an ideal white, B r and B rw Lightness, L* P (L* P = Q+50) and L* N (L* N = 100*B r /B rw ) Lightness, L* P (L* P = Q+50) and L* N (L* N = 100*B r /B rw ) Chroma, C RG, C YB, C Chroma, C RG, C YB, C Colorfulness, M RG, M YB, M Colorfulness, M RG, M YB, M

6. Prediction of Helson-Judd Effect Helson-Judd Effect: “Samples lighter than the background exhibited chroma of the same hue as the source, while samples darker than the background exhibited chroma of the hue of the source’s component.” Helson-Judd Effect: “Samples lighter than the background exhibited chroma of the same hue as the source, while samples darker than the background exhibited chroma of the hue of the source’s component.” The achromatic object colors with Munsell values 5/ and 3/ give blueness perception complementary to the hue of illuminant A (incandescent illuminant); White Background with Y0 = 60 (approximately 8/). The achromatic object colors with Munsell values 5/ and 3/ give blueness perception complementary to the hue of illuminant A (incandescent illuminant); White Background with Y0 = 60 (approximately 8/).

7. Extension of CIE chromatic-adaptation transform The limitation of CIE chromatic-adaptation transform: The limitation of CIE chromatic-adaptation transform: It can be used only when the illuminance of the test field should be the same as that of the reference field in the condition of a light-gray background; The coefficient A is very close to unity. It can be used only when the illuminance of the test field should be the same as that of the reference field in the condition of a light-gray background; The coefficient A is very close to unity. Or the complex procedure is needed for its computation. The coefficient A is derived by a complex successive- approximation method. Or the complex procedure is needed for its computation. The coefficient A is derived by a complex successive- approximation method. The computation is simplified by using the nonlinear color- appearance model. The analytical form of the coefficient A can be obtained. The computation is simplified by using the nonlinear color- appearance model. The analytical form of the coefficient A can be obtained.

8. Two Chromatic-adaptation transforms Lightness-chroma match (Q-C transform) Lightness-chroma match (Q-C transform) Brightness-colorfulness match (B-M transform) Brightness-colorfulness match (B-M transform) The procedure combining of chromatic-adaptation transforms and color-appearance models is given for each transform. The procedure combining of chromatic-adaptation transforms and color-appearance models is given for each transform. These procedure provide not only colorimetric values of the corresponding colors (chromatic- adaptation transform) but also the attributes of color appearance (color-appearance model). This is the advantage of combination of these two. These procedure provide not only colorimetric values of the corresponding colors (chromatic- adaptation transform) but also the attributes of color appearance (color-appearance model). This is the advantage of combination of these two.

9. Experiment & Results Haploscopic matching Haploscopic matching The values of lightness and chroma for B-M match are lower than those for Q-C match. (Fig.3) The values of lightness and chroma for B-M match are lower than those for Q-C match. (Fig.3) The observers can discriminate the difference between Q-C and B-M match, because there are no overlapping region between those two. (Fig.3) The observers can discriminate the difference between Q-C and B-M match, because there are no overlapping region between those two. (Fig.3) The predicated corresponding colors by Q-C and B-M transformation are correlated to observed corresponding colors. (Fig.4 and 5) The predicated corresponding colors by Q-C and B-M transformation are correlated to observed corresponding colors. (Fig.4 and 5)

10. Conclusions The nonlinear color-appearance model was extended to white and light-gray background. The nonlinear color-appearance model was extended to white and light-gray background. The model simplified the complex computations of the chromatic-adaptation transformation. The model simplified the complex computations of the chromatic-adaptation transformation. Two kinds of chromatic-adaptation transform existed. (Q-C and B-M) Two kinds of chromatic-adaptation transform existed. (Q-C and B-M) Any color-appearance model should be combined with chromatic-adaptation transformation. Any color-appearance model should be combined with chromatic-adaptation transformation.

11. Revised Colour-appearance model for related and unrelated colours R. W. G. Hunt The Hunt 91 model

12. Outline Five different visual fields are clearly defined. Five different visual fields are clearly defined. The steps required in the model for both related colors and unrelated colors are described. (Refer to Appendix I and II) The steps required in the model for both related colors and unrelated colors are described. (Refer to Appendix I and II) Parameters Parameters Results for related colors Results for related colors Results for unrelated colors Results for unrelated colors Conclusions Conclusions

13. Parameters Cone responses , ,  (similar to LMS in the RLAB model) Cone responses , ,  (similar to LMS in the RLAB model) Cone responses after adaptation  a,  a,  a Cone responses after adaptation  a,  a,  a Colour difference signal C 1, C 2, C 3 (dissimilar to CIELAB) Colour difference signal C 1, C 2, C 3 (dissimilar to CIELAB) Hue H, Hue composition H C, Hue angle h S Hue H, Hue composition H C, Hue angle h S Yellowness-blueness, M YB, redness-greenness, M RG, and colorfulness M Yellowness-blueness, M YB, redness-greenness, M RG, and colorfulness M Achromatic signal A: the photopic part A a and the scotopic part A s Achromatic signal A: the photopic part A a and the scotopic part A s Saturation s, relative yellowness-blueness, m YB, redness-greenness, m RG Saturation s, relative yellowness-blueness, m YB, redness-greenness, m RG Brightness Q, lightness J Brightness Q, lightness J Chroma C b Chroma C b Whiteness-Blackness Q WB Whiteness-Blackness Q WB

14. Verification for Related Colors The shape of cone response curves after adaptation,  a,  a,  a, is reasonable by comparing response curves predicted by the model with experimental results. (Fig.6) The shape of cone response curves after adaptation,  a,  a,  a, is reasonable by comparing response curves predicted by the model with experimental results. (Fig.6) The brightness, Q, gives predictions in very satisfied agreement with these experimental results. (Fig.7-9) The brightness, Q, gives predictions in very satisfied agreement with these experimental results. (Fig.7-9) The prediction of the hue H, lightness J, chroma C b, of the model is comparable with the interobserver uncertainty. The prediction of the hue H, lightness J, chroma C b, of the model is comparable with the interobserver uncertainty. The model predicts color appearance of special case quite well. The “famous” picture includes a yellow cushion and a person wearing a white blouse. (Fig. 10) The model predicts color appearance of special case quite well. The “famous” picture includes a yellow cushion and a person wearing a white blouse. (Fig. 10) The increase in the sum of brightness and colorfulness is related to the increase in luminance. The model predicts the experimental results well. The increase in the sum of brightness and colorfulness is related to the increase in luminance. The model predicts the experimental results well.

15. Unrelated Colors Although unrelated colors are often seen in completely dark fields, the luminance of adapting field is not zero. Although unrelated colors are often seen in completely dark fields, the luminance of adapting field is not zero. The chromaticity of adapting field is the same as that of the equi-energy stimulus. The chromaticity of adapting field is the same as that of the equi-energy stimulus. Unrelated colors are affected by the previous adapting field or the conditional field. Unrelated colors are affected by the previous adapting field or the conditional field. For unrelated colors, there are no conception of a reference white. For unrelated colors, there are no conception of a reference white. The luminance level of a stimulus has and effect on its apparent hue. This phenomenon is known as the Bezold- Brϋcke hue shift. The luminance level of a stimulus has and effect on its apparent hue. This phenomenon is known as the Bezold- Brϋcke hue shift.

16. Verification for Unrelated Colors The model predicts Bezold-Brϋcke hue shift, which is correlated with Purdy’s results. (Fig. 12) The model predicts Bezold-Brϋcke hue shift, which is correlated with Purdy’s results. (Fig. 12) The model gives the prediction of brightness in satisfactory agreement with Bartleson’s results. (Fig. 13) The model gives the prediction of brightness in satisfactory agreement with Bartleson’s results. (Fig. 13) The colorfulness of unrelated colors is less than that of related colors seen at the same brightness in the mid- photopic range. The model predicts the above results, similar to that found by Pitt and Winter. The colorfulness of unrelated colors is less than that of related colors seen at the same brightness in the mid- photopic range. The model predicts the above results, similar to that found by Pitt and Winter.

17. Conclusions The model describes the contributions of the cones and rods to the achromatic signal, A, appropriately. The model describes the contributions of the cones and rods to the achromatic signal, A, appropriately. For related colors, luminance factor of the background has a great effects on the color appearance. For related colors, luminance factor of the background has a great effects on the color appearance. For unrelated colors, the model predicts the Bezold-Brϋcke hue shift. For unrelated colors, the model predicts the Bezold-Brϋcke hue shift.

18. An improved Predictor of Colourfulness in a model of colour vision R. W. G. Hunt The Hunt 94 model

19. New Predictor for Chroma For light colors, the new chroma, C 94, decreases as the background becomes darker; For light colors, the new chroma, C 94, decreases as the background becomes darker; For media and dark colors, the new chroma, C 94, increases as the background becomes darker; For media and dark colors, the new chroma, C 94, increases as the background becomes darker; Because the revised model predicts appearance consistently for different viewing condition, the new predictor for Chroma is adopted. Because the revised model predicts appearance consistently for different viewing condition, the new predictor for Chroma is adopted. The new chroma, C 94, describes the effects of luminous factor of the background more appropriately. The new chroma, C 94, describes the effects of luminous factor of the background more appropriately.

20. New Predictor for Colorfulness The effect of luminance of the adapting field on colorfulness is overestimated by the Hunt 91 model. The power changes from 1/5.5 (0.18) to 0.15. The effect of luminance of the adapting field on colorfulness is overestimated by the Hunt 91 model. The power changes from 1/5.5 (0.18) to 0.15. New predictor for colorfulness, M94, gives prediction in agreement with the experimental results. New predictor for colorfulness, M94, gives prediction in agreement with the experimental results. M94 results in a smaller dependence of colorfulness on adapting luminance, and allows for the effects of luminance factor of background on the colorfulness. M94 results in a smaller dependence of colorfulness on adapting luminance, and allows for the effects of luminance factor of background on the colorfulness.

21. Image color-appearance specification through extension of CIELAB Mark D. Fairchild & Roy S. Berns The RLAB model

22. Outline An extension of CIELAB, RLAB, incorporates the advantage of CIELAB, a more accurate model of chromatic-adaptation and the capability to adjust for the changes in surround. An extension of CIELAB, RLAB, incorporates the advantage of CIELAB, a more accurate model of chromatic-adaptation and the capability to adjust for the changes in surround. RLAB can be used to calculate the attributes of color appearance. RLAB can be used to calculate the attributes of color appearance. RLAB cab be applied in cross-media reproduction. RLAB cab be applied in cross-media reproduction.

23. CIELAB Advantages Advantages (a) Recommended in 1976; (b) The first approximation to a color-appearance space; (c) An adequate color-appearance model at daylight or nearly daylight illumination. (d) Take chromatic adaptation into account by normalizing tristimulus values XYZ. (d) Take chromatic adaptation into account by normalizing tristimulus values XYZ. Disadvantages Disadvantages (a) Not useful for large changes in viewing conditions. (b) Less accurate because the normalization performed on XYZ rather than fundamental tristimulus values LMS. (c) Not account for color-appearance changes due to changes in illuminant level and surround.

24. Chromatic-adaptation model The model can predicate the Hunt effect and Steven effect, and distinguish the differences between reflective (e.g. prints) and self-luminous (e.g. CRT display). The model can predicate the Hunt effect and Steven effect, and distinguish the differences between reflective (e.g. prints) and self-luminous (e.g. CRT display). The model is formulated as a series of matrix multiplication. The model is formulated as a series of matrix multiplication. M: transform matrix from XYZ to LMS A: the matrix for chromatic-adaptation transformation C: the matrix for calculating post-adaptation signals This term is eliminated in the revised model. This term is eliminated in the revised model.

25. Effects of Surround A dark surround causes all of the colors to appear lighter. The effect is larger for darker colors than for lighter colors, which results a decrease in image contrast. A dark surround causes all of the colors to appear lighter. The effect is larger for darker colors than for lighter colors, which results a decrease in image contrast. Several examples Several examples Cases Gamma Exponents for CIELAB Cases Gamma Exponents for CIELAB photographic prints viewed in average surround 1.0 1/3 photographic prints viewed in average surround 1.0 1/3 projected transparency viewed in dark surround 1.5 1/(3*1.5) = 1/4.5 projected transparency viewed in dark surround 1.5 1/(3*1.5) = 1/4.5 CRT display viewed in dim surround 1.25 1/(3*1.25) = 1/3.75 CRT display viewed in dim surround 1.25 1/(3*1.25) = 1/3.75

26. RLAB R refers to reproduction. R refers to reproduction. Under the reference viewing conditions (CIE illuminant D65, the white luminance 318 cd/m 2, discounting-the-illuminant), the RLAB and CIELAB are identical. Under the reference viewing conditions (CIE illuminant D65, the white luminance 318 cd/m 2, discounting-the-illuminant), the RLAB and CIELAB are identical. Otherwise, the chromatic-adaptation model is used to convert XYZ to the corresponding XYZ under reference viewing conditions. The 1/3 exponents in the CIELAB equation are changed to 1/3.75 for dim surround or 1/4.5 for dark surround. Otherwise, the chromatic-adaptation model is used to convert XYZ to the corresponding XYZ under reference viewing conditions. The 1/3 exponents in the CIELAB equation are changed to 1/3.75 for dim surround or 1/4.5 for dark surround. X Y ZX ref Y ref Z ref RCAM L R a R b R C R h R Exponent 1/3 or 1/3.75 or1/4.5 ERER

27. Model Performance The Hunt pillow demonstration The Hunt pillow demonstration White shirt Vs. yellow pillow White shirt Vs. yellow pillow A experiment was performed to evaluate the performance of RLAB space over cross-media reproduction. A experiment was performed to evaluate the performance of RLAB space over cross-media reproduction. X1Y1Z1X1Y1Z1 X 1,ref Y 1,ref Z 1,ref Reflectance Device colorimetric characterization Color-appearance models Gamut mapping, tone reproduction, etc. X2Y2Z2X2Y2Z2 X 2,ref Y 2,ref Z 2,ref RGB

28. Conclusions The advantages of RLAB The advantages of RLAB Incorporate the advantages of CIELAB; Include a more accurate model of chromatic-adaptation; Adjust for the changes in surround; Adjust for the changes in surround; Can be easily invertible. RLAB can be applied to cross-media reproduction. RLAB can be applied to cross-media reproduction.

29. Refinement of the RLAB color space Mark D. Fairchild The revised RLAB model

30. Outline Visual evaluation of RLAB Visual evaluation of RLAB Refinement of the RLAB equations Refinement of the RLAB equations Summary of RLAB equations Summary of RLAB equations Conclusions Conclusions

31. Visual evaluation of RLAB Four experiments were performed. Four experiments were performed. print-to-print reproduction simple object-color reproduction print-to-CRT reproduction CRT-to-projected slide reproduction RLAB performed the best for three experiments except for the second one. (RLAB performs well for pictorial images and not-so-well for simple patches.) RLAB performed the best for three experiments except for the second one. (RLAB performs well for pictorial images and not-so-well for simple patches.) The reason is that RLAB introduced unwanted shift in the lightness of the samples as the change in luminance level. The reason is that RLAB introduced unwanted shift in the lightness of the samples as the change in luminance level. The solution is to remove the C matrix in the revised RLAB model. The solution is to remove the C matrix in the revised RLAB model.

32. Refinement of the RLAB Equations The C matrix is removed, because it is more detrimental than beneficial. After that, the model can’t predict the Hunt effect and Stevens effect. The C matrix is removed, because it is more detrimental than beneficial. After that, the model can’t predict the Hunt effect and Stevens effect. The D parameter is added to describe the level of discounting-the-illuminant, and the model becomes more flexible. The D parameter is added to describe the level of discounting-the-illuminant, and the model becomes more flexible. Power functions are simplified by discarding the reference white in the standard CIELAB equations. Power functions are simplified by discarding the reference white in the standard CIELAB equations. The M matrix is normalized; The R matrix (M -1 A -1 ) is also normalized. The M matrix is normalized; The R matrix (M -1 A -1 ) is also normalized.

33. Conclusions The revised RLAB performs as well as, or better than more complex color-appearance models, e.g. the Hunt model. The revised RLAB performs as well as, or better than more complex color-appearance models, e.g. the Hunt model. Two drawbacks of the Hunt model: ambiguity or difficulty in determining so many values in the model; the appearance judgments for images depends both on the individual colors, but on the relationship between colors, e.g. image contrast. Two drawbacks of the Hunt model: ambiguity or difficulty in determining so many values in the model; the appearance judgments for images depends both on the individual colors, but on the relationship between colors, e.g. image contrast.