🤖 AI Summary
This work addresses the insufficient accuracy of the Oklab/Oklch color spaces in predicting perceptual color differences, which limits their utility in high-consistency design and interpolation tasks. The authors propose Oklch+, an enhanced color space incorporating a power transformation along the lightness (L) axis and Naka–Rushton–based saturation compression along the chroma (C) axis, yielding a model with only three tunable parameters. Perceptual color differences are approximated using Euclidean distance in this modified space. Optimized on the COMBVD dataset, Oklch+ achieves a STRESS value of 29.09—comparable to CIEDE2000 (29.13)—and further improves to 26.14 on the BFD-P D65 holdout set, substantially outperforming original Oklab (51.45). Thus, Oklch+ approaches the predictive performance of CIEDE2000 while retaining simplicity and interpolability.
📝 Abstract
Oklab and its cylindrical representation Oklch are widely adopted in interpolation and design workflows as perceptually motivated color spaces, but their color difference prediction accuracy falls short of CIEDE2000. We propose Oklch+, a three-parameter extension of Oklab comprising a power transformation on the L-axis and a Naka-Rushton compression on the C-axis, with Euclidean distance computed in the resulting transformed Oklab coordinates. The Naka-Rushton function is bounded in [0,1], reflecting the saturating nature of chroma sensitivity at high colorimetric values. Evaluated on COMBVD -- 3,813 suprathreshold color difference pairs spanning six independent experimental datasets -- Oklch+ achieves STRESS = 29.09, closely matching CIEDE2000 (29.13; difference = 0.04), using only three parameters optimized against color difference data compared to approximately 17 for CIEDE2000. Cross-validation on a held-out BFD-P D65 subset (2,028 pairs) confirms generalization (STRESS = 26.14), with Oklch+ substantially outperforming Oklab (51.45) and achieving STRESS comparable to CIEDE2000 (24.12) on the held-out set. Improvement over Oklab (47.35) is confirmed across all six COMBVD sub-datasets. Because Oklch+ defines a coordinate system in which Euclidean distance approximates perceptual distance, linear interpolation in the transformed space offers substantially improved perceptual uniformity relative to Oklab. Current evaluation is limited to the sRGB-centered COMBVD dataset; validation in high-chroma regions with empirical observer-rated discrimination data remains future work.