We propose a method to improve existing color-difference formulas with additional visual data from color discrimination experiments. Color-difference formulas are treated as mean functions of Gaussian processes, and the visual data are considered as observations of these processes. Gaussian process regression is applied to predict unknown color differences. The method was evaluated with a combination of the CIEDE2000 color-difference formula and the RIT-DuPont dataset. The standardized residual sum of squares (STRESS) index between visual and computed color differences was determined for several sets of visual data. The results show a STRESS index of 6.94 (CIEDE2000: 19.47) for the RIT-DuPont dataset. The prediction performance on other visual data (BFD, Leeds, Witt) is not significantly different from CIEDE2000 at a 95% confidence level.
Ingmar Lissner, Philipp Urban, "Improving Color-Difference Formulas Using Visual Data" in Proc. IS&T CGIV 2010/MCS'10 5th European Conf. on Colour in Graphics, Imaging, and Vision 12th Int'l Symp. on Multispectral Colour Science, 2010, pp 483 - 488, https://doi.org/10.2352/CGIV.2010.5.1.art00075