Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color

• URL: http://arxiv.org/abs/2511.17133v1
• Date: Fri, 21 Nov 2025 10:49:04 GMT
• Title: Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color
• Abstract summary: Traditional in-camera colorimetric mapping relies on correlated color temperature (CCT)-based transforms optimized for Planckian illuminants such as CIE A and D65.<n>Modern lighting technologies can deviate substantially from the Planckian locus, exposing the limitations of relying on conventional one-dimensional CCT for illumination characterization.<n>We replace conventional CCT with a lightweight multi-layer perceptron (MLP) that accommodates 2D chromaticity features for robust colorimetric mapping under non-Planckian illuminants.
• Score: 14.28369643988171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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