🤖 AI Summary
This work addresses the limitations of existing color spaces in accurately modeling human perceptual sensitivity to color differences in UI design, particularly in scenarios such as color palette generation, design token derivation, and light/dark mode adaptation, where high-precision, reversible, and engineering-friendly solutions are lacking. The authors propose HELMLAB—a 72-parameter analytical color space that uniquely integrates Fourier-based hue correction and the Helmholtz–Kohlrausch luminance effect into a fully invertible transformation. It enforces strict neutrality at a = b = 0 and employs a rigid rotation to preserve hue alignment and perceptual uniformity. Evaluated on the COMBVD dataset, HELMLAB achieves a STRESS value of 23.22—20.4% lower than CIEDE2000—with round-trip errors below 10⁻¹⁴. The framework is accompanied by practical tools for gamut mapping, design token export, and adaptive light/dark mode conversion.
📝 Abstract
We present HELMLAB, a 72-parameter analytical color space for UI design systems. The forward transform maps CIE XYZ to a perceptually-organized Lab representation through learned matrices, per-channel power compression, Fourier hue correction, and embedded Helmholtz-Kohlrausch lightness adjustment. A post-pipeline neutral correction guarantees that achromatic colors map to a=b=0 (chroma < 10^-6), and a rigid rotation of the chromatic plane improves hue-angle alignment without affecting the distance metric, which is invariant under isometries. On the COMBVD dataset (3,813 color pairs), HELMLAB achieves a STRESS of 23.22, a 20.4% reduction from CIEDE2000 (29.18). Cross-validation on He et al. 2022 and MacAdam 1974 shows competitive cross-dataset performance. The transform is invertible with round-trip errors below 10^-14. Gamut mapping, design-token export, and dark/light mode adaptation utilities are included for use in web and mobile design systems.