Evaluation of Color Difference Models for Wide Color Gamut and High Dynamic Range.

Abstract

Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented in current datasets. This gap highlights the need to validate CDMs across WCG and HDR. Moreover, the non-geodesic structure of perceptual color space necessitates datasets covering CDs of various magnitudes, while most existing datasets emphasize only small and threshold CDs. To address this, we collected a new dataset encompassing a broad range of CDs in WCG and HDR contexts and developed a novel CDM fitted to these data. Benchmarking various CDMs using STRESS and significant error fractions on both new and established datasets reveals that CAM16-UCS with power correction is the most versatile model, delivering strong average performance across WCG colors up to 1611 cd/m². However, even the best CDM fails to achieve the desired accuracy limits and yields significant errors. CAM16-UCS, though promising, requires further refinement, particularly in its power correction component to better capture the non-geodesic structure of perceptual color space.