为UI设计系统打造可精准预测色差的新型色彩空间。
Helmlab: A Two-Space Family of Analytical, Data-Driven Color Spaces for UI Design Systems
- 基于11阶段分析结构，构建两类专精色彩空间。
- 色差预测准确率较CIEDE2000提升23%，在多个数据集领先。
- 适合需要高保真配色与自动调色的界面设计师和开发者。
我们提出Helmlab，一个包含两个专用色彩空间的家族，共享统一的11阶段分析结构：用于色差预测优化的MetricSpace（72参数），以及用于渐变与配色生成优化的GenSpace（44参数）。前向变换通过学习矩阵、通道幂压缩、傅里叶色调校正及嵌入式赫尔姆霍兹-科尔劳施亮度调整，将CIE XYZ映射至感知有序的Lab表示。后处理中性校正使灰轴色度低于1e-5（21步梯度），刚性旋转色平面改善色调对齐，不影响距离度量（在等距变换下不变）。在COMBVD数据集（3,813对）上，MetricSpace v21 STRESS为22.48，比CIEDE2000（29.20）降低23%；在独立测试的MacAdam 1974数据集上得分为19.51（CIEDE2000: 22.13；CAM16-UCS最佳为18.71）；在自收集的3,552项屏幕条件判断集中得分为23.26，远优于CIEDE2000的62.54。在学术数据集He et al. 2022（82个3D打印样本对）中，MetricSpace得分为35.9，略逊于CIEDE2000的32.6，此回归结果已承认。三组主要数据集平均得分21.75，优于次优基线CIECAM02-UCS的35.98。GenSpace v0.11.1牺牲部分距离精度以换取生成质量，在跨sRGB、P3、Rec.2020的90项指标、3,038对渐变/配色基准中，胜出65项优于OKLab。变换可逆，往返误差低于1e-13。生产级实现已发布于PyPI、npm、Color.js（PR 722已合并）及PostCSS插件。
原文摘要 · Abstract (English)
We present Helmlab, a family of two purpose-built color spaces for UI design systems sharing a common 11-stage analytical structure: MetricSpace, a 72-parameter space optimized for color-difference prediction, and GenSpace, a 44-parameter space optimized for gradient and palette generation. 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 holds gray-axis chroma below 1e-5 on a 21-step ramp, and a rigid rotation of the chromatic plane improves hue-angle alignment without affecting the distance metric (which is invariant under isometries). On COMBVD (3,813 color pairs), MetricSpace v21 achieves STRESS 22.48, a 23 percent reduction from CIEDE2000 (29.20). On the held-out MacAdam 1974 dataset it scores 19.51 (CIEDE2000: 22.13; CAM16-UCS leads at 18.71). On a self-collected 3,552-judgement screen-condition set it scores 23.26 vs 62.54 for CIEDE2000. On academic He et al. 2022 (82 3D-printed pairs) MetricSpace scores 35.9 vs CIEDE2000 32.6, a regression we own. Averaging the three primary datasets, MetricSpace scores 21.75 vs the next-best baseline CIECAM02-UCS at 35.98. GenSpace v0.11.1 trades distance accuracy for generation quality: on a 90-metric, 3,038-pair gradient/palette benchmark across sRGB, P3, and Rec.2020, it wins 65 of 90 vs OKLab. The transform is invertible with round-trip errors below 1e-13. Production implementations ship on PyPI, npm, Color.js (PR 722, merged), and as a PostCSS plugin.
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