HELMLAB：一种分析性、数据驱动的颜色空间用于UI设计系统的感知距离
HELMLAB: An Analytical, Data-Driven Color Space for Perceptual Distance in UI Design Systems
AI总结：
HELMLAB是一种用于UI设计系统的72参数颜色空间，通过数据驱动方法优化颜色感知距离，实现更精确的颜色匹配和设计应用。
摘要：
我们介绍了HELMLAB，一种具有72个参数的分析性颜色空间，用于UI设计系统。正向变换通过学习的矩阵、每通道功率压缩、傅里叶色调校正以及嵌入的赫尔姆霍茨-科尔拉什光度校正，将CIE XYZ映射到一种按感知组织排列的Lab表示。后流水线中性校正确保无色颜色映射到a=b=0（色度<10^-6），而对色度平面的刚性旋转改进了色调角对齐，而不影响距离度量，该度量在等距下是不变的。在COMBVD数据集（3,813对颜色）上，HELMLAB实现了STRESS为23.30，比CIEDE2000（29.18）减少了20.2%。通过蓝带重新拟合并使用子数据集惩罚，蓝-青区域的梯度非均匀性减少了8.9倍，仅以+0.08 STRESS为代价。在He等人2022年和MacAdam 1974年的交叉验证上展示了具有竞争力的跨数据集性能。该变换是可逆的，往返误差低于10^-14。包含色域映射、设计令牌导出和明/暗模式适应工具，用于网页和移动设计系统。
英文摘要：
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 23.30, a 20.2% reduction from CIEDE2000 (29.18). A blue-band refit with sub-dataset penalties reduces gradient non-uniformity in the blue-cyan region by 8.9x at a cost of only +0.08 STRESS. 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.
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