Evaluation of Color Difference Prediction with CIECAM16 using CIE 2- and 10-degree Observers

Cheng Gao, Ming Ronnier Luo, Michael R. Pointer, Changjun Li
2023 Journal of Imaging Science and Technology  
CIE has recently recommended a new color appearance model CIECAM16 to replace CIECAM02. It was also intended to recommend a uniform color space (UCS) based on CIECAM16, CIECAM16-UCS, for predicting color difference. However, there was a debate as to whether CIECAM16 can be used to predict color difference, since it takes tristimulus values calculated using the CIE 2 • observer as input, while tristimulus values for color difference are usually calculated using the CIE 10 • observer, as in the
more » ... EDE2000 formula. Thus, further evidence is needed before CAM16-UCS can be recommended by the CIE as uniform color space. In this paper, we investigate the likely difference in color difference prediction when using CAM16-UCS, and its potential statistical significance. Firstly, the reflectance of each pair of color samples in the visual color difference datasets: BFD-P, Leeds, RIT-DuPont and Witt was generated based on the given tristimulus values. Then, color difference E 2,F and E 10,F , with F being the CAM16-UCS, can be computed under any illuminant using the 2 • and 10 • observers respectively. Comparison results showed that the difference between E 2,F and E 10,F was not statistically significant. Finally, both E 2,F and E 10,F were used to predict the visual color difference DV and the STRESS values between E 2,F and DV , and between E 10,F and DV respectively. Statistical tests showed that the differences between E 2,F and DV , and between E 10,F and DV were not significant. Hence this study shows that CAM16-UCS can be reliably used for predicting color difference. The findings are valuable for CIE to recommend CAM16-UCS as a uniform color space. Currently CIE TC1-98 is investigating the establishment of a new colorimetric system based on cone response tristimulus values LMS. It is expected that the reflectance datasets generated from the color difference datasets will be useful for the evaluation of any new colorimetric system.
doi:10.2352/j.imagingsci.technol.2023.67.2.020405 fatcat:x6x75uizxjfrzajwvroenjasc4