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Evaluating the perceived quality of spectral images

We introduce a new strategy to evaluate the perceived quality of spectral image reproductions. We rely on the idea that spectral image difference can be computed by averaging scores of a color image-difference measure, computed for various viewing conditions. Two approaches are proposed to efficiently approximate this quality prediction by taking advantage of image-difference redundancies across illuminants. Our results suggest that, for the evaluated distortions, image-difference features extracted from the lightness component vary much less across illuminants than those from chroma and hue components. For our setup, we could reduce the numerical effort to compute the original prediction to 1.3%, without impairing accuracy.