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Home , Brightness, Chromaticity, Colorfulness, Colorimetry, Color appearance model, Color constancy

A Pictorial Review of Appearance Models

Mark D. Fairchild and Lisa Reniff Munsell Color Science Laboratory, Center for Imaging Science, Rochester Institute of Technology, Rochester, New York

Abstract can be efficiently implemented. The process is then reversed for the output viewing conditions and device characteriza- Color-appearance models are simultaneously a topic of tion to reproduce a matching image within physical limita- much recent research and a key link in the chain of color tions. (Note: If a transform is used to management systems. While the application and testing of account for viewing conditions, the process can be com- color appearance models for device-independent color im- pleted, but the intermediate appearance correlates are never aging applications has been previously presented, the vi- available.) sual significance of the differences between model predic- tions is not often displayed. This poster presents an array of images designed to illustrate key differences between various color appearance models. A total of 15 color ap- Input Device pearance models (or variations of models) are presented Input Device Colorimetric Characterization and compared. Device Independent Chromatic Adaptation & Introduction Color Appearance Models Viewing Conditions Independent Space CIE XYZ tristimulus values specify color matches between Mapping, Preferences, Spatial two stimuli evaluated in identical viewing conditions by Operations, Tone Reproduction, etc. Viewing Conditions an average observer. While CIE tristimulus values have Independent Space proven their immense value for the prediction of color Chromatic Adaptation & Color Appearance Models matches over the last 65 years, by themselves they tell us Device Independent nothing about the appearance of the stimuli. Color appear- Color Space ance models extend tristimulus values by performing trans- Output Device Colorimetric Characterization formations on them based on additional information about Output Device the state of chromatic adaptation, mode of viewing, lumi- nance level, background, surround, etc. A allows a stimulus, in given viewing conditions, to Figure 1. The process of cross-media color reproduction. be numerically described with correlates of perceptual at- tributes such as , , , chroma, Technological advances in the last decade have accen- and . In cross-media color reproduction applications, tuated the need for a useful color appearance model. While color matches must be made across changes in illumina- technology demands a model, science has yet to develop a tion level and color, point, surround, viewing mode, single answer to this problem (and perhaps never will!). as well as other features of the viewing environment and Thus the formulation, testing, and application of color ap- images. Thus, tristimulus matches cease to be appearance pearance models have been active areas of research. While matches and color appearance models (or at least portions the equations and numerical results of tests are often pre- thereof) are required. Figure 1 shows a flow chart of the sented and discussed, an intuitive visual appreciation of general process of cross-media color reproduction. Figure the differences between models is generally not provided. 1 also illustrates the process followed in the generation of This poster provides a visual comparison of the predictions images for this poster and the process that must be imple- of 15 different color appearance models, chromatic adap- mented within systems. tation transforms, or variations thereof, for three different The process begins with the colorimetric calibration changes in viewing conditions. and characterization of the device producing or acquiring the original image. This allows the device coordinates (e.g., Models RGB) of the original image to be transformed into a de- vice-independent color space (e.g., CIE XYZ). The next A significant number of color appearance models and chro- step requires information about the viewing conditions and matic adaptation transforms have been formulated over the a color appearance model to transform from device-inde- past century (most within the last 20 years) that can be ap- pendent coordinates to a viewing-condition independent plied to the problems of cross-media color reproduction. space that specifies the image appearance (e.g., lightness, While it is impossible to include all of the models that have chroma, and hue). At this point image editing, color prefer- been published, a representative sampling of different types ence adjustments, gamut mapping, and other such processes of models was selected including those most commonly

Chapter I—Color Appearance—127 used in current imaging applications. Some of the models The demonstration images were produced using a can also be implemented with different interpretations of single image made up of a montage of two pictorial scenes the viewing conditions. Multiple examples of these have with patches from the Macbeth Color Checker Chart pasted been included to illustrate the function of the models. along the edge for comparison. All images were produced for hypothetical viewing conditions as defined above. To The models presented include: truly evaluate the performance of the various models, the CIE 1931 XYZ (i.e., no model at all) original and reproduced images must be viewed in the ap- CIELAB1 propriate conditions with sufficient adaptation time. De- CIELUV1 spite this limitation, the images are of significant value in LABHNU22 comparing the relative characteristics of the various mod- von Kries3 els and they certainly contribute to a better Spectrally Sharpened von Kries4 of the models’ formulations. All prints were made using a ATD 5 Fujix Pictrography 3000 digital printer. All calculations Nayatani et al.6 were performed with double-precision floating-point pro- LLAB7 cedures to avoid quantization artifacts in intermediate im- Hunt8 [Discounting] ages. In some cases the floating-point calculations were Hunt8 [No Discounting] performed on the images themselves while in other cases, Hunt8 [Inc. Adaptation, No Helson-Judd Effect] floating-point calculations were used to construct 24-bit RLAB9 [Discounting] precision three-dimensional LUTs used for image transfor- RLAB9 [Partial Discounting] mations via interpolation. RLAB9 [No Discounting]. Viewing Condition Transforms Psychophysical Tests Given the important role of color appearance models in The first viewing condition explored is a change in white- cross-media color reproduction, it is fair to ask which one point from that of CIE Illuminant A to CIE should be used. Unfortunately, that question is not easily an- at constant . This, rather large, swered. A perfect color appearance model is not available and change in was chosen to better illustrate the each model has advantages and disadvantages. There are significant differences between models. Also it is not un- two CIE technical committees (TC1-27 and TC1-34) actively usual to encounter such large white-point changes in desk- evaluating various models. Both committees have published top color imaging applications (e.g., 9300K CRT display guidelines for coordinated research10,11 and are currently col- and tungsten illumination for prints). All 15 transforma- lecting and evaluating additional data. These committees are tions are presented for this white-point change. unable to recommend a single color appearance model as The second viewing condition explored is a change in superior for specific applications. However, recognizing the adapting luminance level from 100 cd/m2 to 10000 cd/m2 urgent industrial need, TC1-34 has been charged with for- at a constant D65 white point. Many of the models do not mulating a single CIE color appearance model incorporat- predict changes with luminance level so they are all repre- ing the best features of published models. This model will sented by a single image (CIE XYZ tristimulus match). The continue to be tested, and perhaps refined, as additional scien- predictions of the models with luminance dependencies tific data become available. However, it is hoped that a single (ATD, LLAB, Nayatani, Hunt, RLAB) are presented and CIE model will promote uniformity of practice and help solve contrasted. some technical and commercial problems. TC1-34 hopes The final viewing condition evaluated is a change in to publish this model during 1997. the surround from an average surround A variety of psychophysical evaluations of color ap- (typical for print viewing) to a dark surround (typical for pearance models in cross-media image reproduction appli- projected transparencies). Again, this extreme surround cations have been completed.9,12-15 While the results cannot change was chosen to accentuate the differences between be briefly summarized and no single model works best in the models. Many of the models do not predict changes all experiments, general trends regarding each model have with surround relative luminance so they will be represented been found. These are briefly summarized below. There by a single image. The predictions of the models with sur- are many details behind these general summaries and the round dependencies (LLAB, Hunt, RLAB) are presented. original references should be reviewed prior to making In all cases, lightness-chroma matches were calculated strong conclusions. rather than brightness-colorfulness matches. This is because brightness-colorfulness matches are of no practical value CIE XYZ: Tristimulus values are not a color appearance for color reproduction applications. Some models (ATD, specification. Their use is limited to identical viewing con- LLAB) do not have explicit predictors for chroma. In these ditions for original and reproduction. cases, the most appropriate approximation was used. The CIELAB: CIELAB works surprisingly well, but shows some chromatic adaptation transforms (von Kries, etc.) do not inaccuracies in the due to it’s application of a include appearance correlates. Thus they were used simply von Kries-type adaptation transform to XYZ tristimulus as transformations to predict corresponding with the values rather than cone responses. understanding that the data on which they are based con- sist of lightness-chroma matches. This can easily be proven CIELUV: CIELUV produces completely unacceptable per- to be true since the adaptation transforms do not include formance due to its subtractive adaptation transform that luminance dependencies. often shifts colors out of gamut and distorts hues.

128—Recent Progress in Color Processing LABHNU2: LABHNU2 is similar to CIELAB in spacing, round changes, then the RLAB model can be used with- but incorporates an adaptation transform similar to that of out too much added complexity. CIELUV. It thus performs similarly to CIELUV. 5. Lastly, if a full range of appearance phenomena and von Kries: While the von Kries model is only an adapta- wide range of viewing conditions (e.g., very high or low tion transform, it works quite well and can be used with ) must be addressed, then the Hunt model CIELAB to improve it. should be used. The Hunt model should be used if brightness and colorfulness predictors are required (e.g., Spectrally Sharpened von Kries: This technique apparently overall quality metrics for projected transparencies). predicts higher degrees of .4 However, this does not necessarily correlate well with human visual ob- As a last note, it is expected that the forthcoming CIE servations for which color constancy is quite poor. color appearance model will be sophisticated enough to ATD: The ATD model can potentially work as well as the address the same issues as the Hunt model (perhaps more von Kries transform. It does not include appropriate ap- accurately) and also incorporate a simplified and compat- pearance predictors (i.e., lightness, chroma, and hue) and ible version that is similar, in concept, to RLAB. requires some interpretation beyond published equations. Nayatani et al.: The Nayatani et al. model performs poorly Acknowledgments for images due to its intrinsic prediction of a strong Helson- Judd effect, which is not observed. This research was supported by the NSF-NYS/IUCRC and LLAB: LLAB is a relatively new model that appears to have NYSSTF- Center for Electronic Imaging Systems. a very good adaptation transform. However, it includes pre- dictors of lightness, colorfulness, and hue rather than - References ness, chroma, and hue and is not analytically invertible. 1. , CIE Publ. No. 15.2, Central Bureau of the CIE, These factors limit it’s usefulness. Vienna, (1986). Hunt: The Hunt model generally performs quite well. Al- 2. K. Richter, Cube-root Color Spaces and Chromatic Adapta- though sometimes its complexity requires some careful tion, Color Res. Appl. 5: 25-43 (1980). specification of various parameters in order to obtain reli- 3. J. von Kries, Chromatic Adaptation, Festschrift der Albrecht- able results. Generally, its complexity is not warranted for Ludwig-Universität, (Fribourg) (1902) (Translation: D. L. MacAdam, Sources of Color Science, MIT Press, Cam- imaging applications. It is also not analytically invertible. bridge, (1970)). RLAB: RLAB has worked quite well for imaging applica- 4. G. D. Finlayson, M. S. Drew, and B. V. Funt, Color Con- tions. However, it’s adjustments for changes in surround stancy: Generalized Diagonal Transforms Suffice, J. Opt. relative luminance might be too strong for some changes Soc. Am. A 11: 3011-3019 (1994). 5. S. L. Guth, ATD Model for I: Background, SPIE in viewing conditions. This can be fixed by using smaller Vol. 2170. 149-152 (1994); (See page 190, this publication). changes in the RLAB exponents. 6. Y. Nayatani, H. Sobagaki, K. Hashimoto, and T. Yano, Light- ness Dependency of Chroma Scales of a Nonlinear Color- Conclusions Appearance Model and its Latest Formulation, Color Res. Appl. 20: 156-167 (1995). The aim of this poster was to promote a better understand- 7. M. R. Luo, M.-C. Lo, and W.-G. Kuo, The LLAB(l:c) Colour Model, Color Res. Appl. 21: submitted (1996). ing of various color appearance models by displaying pic- 8. R. W. G. Hunt, An Improved Predictor of Colourfulness in a torial images illustrating various predictions. Unfortunately, Model of Colour Vision, Color Res. Appl. 19: 23-26 (1994). cost and time constraints precluded the production of ac- 9. M.D. Fairchild, Refinement of the RLAB Color Space, Color curate color prints to be inserted into the proceedings. Res. Appl. 21: in press (1996). Color appearance models do make significant contri- 10. P. J. Alessi, CIE Guidelines for Coordinated Research on butions to the quality of cross-media color reproduction Evaluation of Colour Appearance Models for Reflection Print when used with care (like any process). Given the large and Self-Luminous Display Comparisons, Color Res. Appl. 19: 48-58 (1994). and often confusing variety of choices available, a few 11. M.D. Fairchild, Testing Colour-Appearance Models: Guide simple recommendations might be useful. These are pre- lines for Coordinated Research, Color Res. Appl. 20: 262- sented in order of increasing complexity and it should be 267(1995). noted that increasingly careful control of the viewing con- 12. K. M. Braun, M.D. Fairchild, and P. J. Alessi, Viewing En- ditions is also required. vironments for Cross-Media Image Comparisons, Color Res. Appl. 21: 6-17 (1996). 1. If possible, it is preferable to equate the viewing con- 13. K. M. Braun and M.D. Fairchild, Testing Five Color Ap- ditions such that simple tristimulus matches are also pearance Models for Changes in Viewing Conditions, Color appearance matches. Res. Appl. 21: submitted (1996). 2. If a white point change is necessary, CIELAB can be 14. K. M. Braun, and M.D. Fairchild, Psychophysical Genera- tion of Matching Images for Cross-Media Color Reproduc- used as a reasonable first-order approximation of an tion, IS&T/SID 4th Color Imaging Conference (1996); (See appearance model. page 136 this publication). 3. If CIELAB is found to be inadequate, it can be enhanced 15. M.-C. Lo, M. R. Luo, and P.A. Rhodes, Evaluating Colour by using a von Kries chromatic adaptation transform (on Models’ Performance Between Monitor and Print Images, cone responses) to a reference viewing condition. Color Res. Appl. 21: 277-291 (1996). 4. If a more flexible adaptation model is required (e.g. published previously in the IS&T 1996 Color Imaging hard-copy to soft-copy changes) and/or their are sur- Conference Proceedings, page 97

Chapter I—Color Appearance—129