ChameleonTuner: Automatic ISP Color Tuning in Subjective Scenarios

Tuning parameters in camera image signal processing (ISP) modules, such as 3D lookup tables (3D LUTs), is essential for generating high-quality images. In subjective scenarios, variations in field-of-view (FoV) and point-of-view (PoV) between source and target images introduce geometric misalignments, limiting the effectiveness of existing calibration methods that rely on pixel-wise alignment. We propose ChameleonTuner, a novel framework incorporating region-level color correspondences to handle such FoV/PoV variations. Our method leverages multi-objective evolutionary search for 3D LUT optimization, offering a controllable and interpretable alternative to neural network-based approaches. Extensive experiments demonstrate that our proposed framework is effective and efficient in challenging subjective scenarios, while remaining competitive on standard benchmarks without FoV/PoV variations. Compared to the state-of-the-art 3DLUT optimization baseline, ChameleonTuner achieves a 26.7\% PSNR gain and a 49.7\% reduction in $\Delta E$ on DPED, a subjective cross-device color calibration dataset with mild FoV/PoV variations.