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In the 1950s, Barlow and Attneave hypothesised a link between biologicalvision and information maximisation. Following Shannon, information was definedusing the probability of natural images. A number of physiological andpsychophysical phenomena have been derived ever since from principles likeinfo-max, efficient coding, or optimal denoising. However, it remains unclearhow this link is expressed in mathematical terms from image probability. First,classical derivations were subjected to strong assumptions on the probabilitymodels and on the behaviour of the sensors. Moreover, the direct evaluation ofthe hypothesis was limited by the inability of the classical image models todeliver accurate estimates of the probability. In this work we directlyevaluate image probabilities using an advanced generative model for naturalimages, and we analyse how probability-related factors can be combined topredict human perception via sensitivity of state-of-the-art subjective imagequality metrics. We use information theory and regression analysis to find acombination of just two probability-related factors that achieves 0.8correlation with subjective metrics. This probability-based sensitivity ispsychophysically validated by reproducing the basic trends of the ContrastSensitivity Function, its suprathreshold variation, and trends of the Weber-lawand masking.

Disentangling the Link Between Image Statistics and Human Perception