Abstract

The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor $k_L=1$ to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize $k_L$ in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor $k_L=1$. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A 25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor ${\mathrm{k}}_{\mathrm{L}}=2$ in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas.

© 2013 Optical Society of America

Relative significance of the terms in the CIEDE2000 and CIE94 color-difference formulas

Manuel Melgosa, Rafael Huertas, and Roy S. Berns
J. Opt. Soc. Am. A 21(12) 2269-2275 (2004)

Performance of recent advanced color-difference formulas using the standardized residual sum of squares index

Manuel Melgosa, Rafael Huertas, and Roy S. Berns
J. Opt. Soc. Am. A 25(7) 1828-1834 (2008)

Performance of select color-difference formulas in the blue region

R. Shamey, R. Cao, T. Tomasino, S. S. H. Zaidy, K. Iqbal, J. Lin, and S. G. Lee
J. Opt. Soc. Am. A 31(6) 1328-1336 (2014)

Embedding non-Euclidean color spaces into Euclidean color spaces with minimal isometric disagreement

Philipp Urban, Mitchell R. Rosen, Roy S. Berns, and Dierk Schleicher
J. Opt. Soc. Am. A 24(6) 1516-1528 (2007)

Color-difference formula for evaluating color pairs with no separation: ΔE_NS

Fereshteh Mirjalili, Ming Ronnier Luo, Guihua Cui, and Jan Morovic
J. Opt. Soc. Am. A 36(5) 789-799 (2019)