用二维色度取代单一色温，提升相机色彩还原精度
Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color
- 改用二维色度空间替代传统色温，更准确描述非普朗克光源
- 在多种LED光照下，色彩还原误差平均降低22%
- 轻量MLP模型支持实时部署，兼容旧光源与多光源场景
传统相机色彩映射依赖相关色温（CCT）在普朗克轨迹上的插值，针对如CIE A和D65等普朗克光源校准。但现代LED光源常偏离普朗克轨迹，暴露了仅用一维CCT表征光照的局限性。本文证明，从1维CCT（在普朗克轨迹上）转向2维色度空间（偏离普朗克轨迹）可显著提升各类映射方法的色彩准确性。我们用包含代表性LED光源的灯箱校准流程，训练了一个轻量级多层感知机（MLP），利用2维色度特征实现对非普朗克光源的鲁棒色彩映射。在多种LED光照场景中验证，该方法平均降低22%的角再现误差，保持与传统光源的向后兼容性，支持多光源场景，并可在相机端实现实时运行，计算开销几乎为零。
原文摘要 · Abstract (English)
Traditional in-camera colorimetric mapping relies on correlated color temperature (CCT)-based interpolation between pre-calibrated transforms optimized for Planckian illuminants such as CIE A and D65. However, modern lighting technologies such as LEDs can deviate substantially from the Planckian locus, exposing the limitations of relying on conventional one-dimensional CCT for illumination characterization. This paper demonstrates that transitioning from 1D CCT (on the Planckian locus) to a 2D chromaticity space (off the Planckian locus) improves colorimetric accuracy across various mapping approaches. In addition, we replace conventional CCT interpolation with a lightweight multi-layer perceptron (MLP) that leverages 2D chromaticity features for robust colorimetric mapping under non-Planckian illuminants. A lightbox-based calibration procedure incorporating representative LED sources is used to train our MLP. Validated across diverse LED lighting, our method reduces angular reproduction error by 22% on average in LED-lit scenes, maintains backward compatibility with traditional illuminants, accommodates multi-illuminant scenes, and supports real-time in-camera deployment with negligible additional computational cost.
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