CD2: Combined Distances of Contrast Distributions for Image Quality Analysis

Sascha Xu, Jan Bauer, Benjamin Axmann, Wolfgang Maass

Abstract: The quality of visual input impacts both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually they are applied freely and unsupervised. We propose a novel method called CD $$^2$$ to protect against errors that arise during image processing. It is based on distributions of image contrast and custom distance functions which capture the effect of noise, compression, etc. CD $$^2$$ achieves excellent performance on image quality analysis benchmarks and in a separate user test with only a small data and computation overhead.