  In the 1950s, Barlow and Attneave hypothesised a link between biological
vision and information maximisation. Following Shannon, information was defined
using the probability of natural images. A number of physiological and
psychophysical phenomena have been derived ever since from principles like
info-max, efficient coding, or optimal denoising. However, it remains unclear
how this link is expressed in mathematical terms from image probability. First,
classical derivations were subjected to strong assumptions on the probability
models and on the behaviour of the sensors. Moreover, the direct evaluation of
the hypothesis was limited by the inability of the classical image models to
deliver accurate estimates of the probability. In this work we directly
evaluate image probabilities using an advanced generative model for natural
images, and we analyse how probability-related factors can be combined to
predict human perception via sensitivity of state-of-the-art subjective image
quality metrics. We use information theory and regression analysis to find a
combination of just two probability-related factors that achieves 0.8
correlation with subjective metrics. This probability-based sensitivity is
psychophysically validated by reproducing the basic trends of the Contrast
Sensitivity Function, its suprathreshold variation, and trends of the Weber-law
and masking.
