SAAF-SVR for computational color constancy

Variations in illumination conditions critically degrade color fidelity in digital images, thereby compromising the accuracy of downstream computer vision tasks. Building upon these historical principles, this paper proposes a self-attention autoencoding feature support vector regression algorithm. The method extracts probability distributions in the luminance-red-green color space as primitive features, reconstructs them through a self-attention augmented autoencoder, and deploys support vector regression for illumination estimation. Experimental validation demonstrates superior robustness against noise and illumination diversity compared to feature-based alternatives. On the linear GreyBall SFU dataset, our method achieves an average 64.4% reduction across six key error metrics relative to the minimum values from all eight comparative methods. On the Cube++ dataset, it yields an average 44.9% reduction across six error metrics relative to the minimum values from all six comparative methods.