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The CIECAM02 color‐appearance model enjoys popularity in scientific research and industrial applications since it was recommended by the CIE in 2002. However, it has been found that computational failures can occur in certain cases such as during the image processing of cross‐media color reproduction applications. Some proposals have been developed to repair the CIECAM02 model. However, all the proposals developed have the same structure as the original CIECAM02 model and solve the problems concerned at the expense of losing accuracy of predicted visual data compared with the original model. In this article, the structure of the CIECAM02 model is changed and the color and luminance adaptations to the illuminant are completed in the same space rather than in two different spaces, as in the original CIECAM02 model. It has been found that the new model (named CAM16) not only overcomes the previous problems, but also the performance in predicting the visual results is as good as if not better than that of the original CIECAM02 model. Furthermore the new CAM16 model is simpler than the original CIECAM02 model. In addition, if considering only chromatic adaptation, a new transformation, CAT16, is proposed to replace the previous CAT02 transformation. Finally, the new CAM16‐UCS uniform color space is proposed to replace the previous CAM02‐UCS space. A new complete solution for color‐appearance prediction and color‐difference evaluation can now be offered.

A maximum gamut for real surface colours has been derived from the analysis of the colour coordinates of 4089 samples. The gamut has been derived in both the CIE 1976 L* u* v* colour space and the CIE 1976 L* a* b* colour space. The data are compared with the gamut produced by a typical dye set used in a photographic colour paper, and with the gamut of a typical television‐receiver display tube. Comparison with printing inks is difficult because of the large number available, but a large set of ink colours was used in deriving the maximum real‐colour gamut.

The determination of the long-term memory colours of objects has been the subject of investigation for many years. Colour acceptance boundaries have been determined from the visual assessments of objects under variable illumination or by presenting manipulated images of objects on a calibrated computer display. However, a systematic and quantitative rating of the colour of real objects with respect to memory colour is not available at this moment. In this article, nine familiar real objects with colours distributed around the hue circle were positioned in a specially designed LED illumination box. For each object, approximately hundred real illumination spectra were synthesized in a random order keeping the luminance of the object approximately constant. Observers were asked to rate, on a five-point scale, the similarity of the perceived object colour to their idea of what the object looked like in reality. By avoiding specular reflections, the observer was unable to identify any clues as to the colour of the illumination. For each object, similarity ratings showed a good intraobserver and interobserver agreement. The ratings of all the observers were pooled and successfully modeled in IPT colour space by a bivariate Gaussian distribution. It was found that the chromaticity corresponding to the highest rating tended to be shifted toward higher chroma in comparison with the chromaticity calculated under D65 illumination. The bivariate distributions could be very useful in applications where the quantitative evaluation of the colour appearance of an object stimulus is required, such as in the evaluation of the colour rendering capabilities of a light source.

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