  Tractable models of human perception have proved to be challenging to build.
Hand-designed models such as MS-SSIM remain popular predictors of human image
quality judgements due to their simplicity and speed. Recent modern deep
learning approaches can perform better, but they rely on supervised data which
can be costly to gather: large sets of class labels such as ImageNet, image
quality ratings, or both. We combine recent advances in information-theoretic
objective functions with a computational architecture informed by the
physiology of the human visual system and unsupervised training on pairs of
video frames, yielding our Perceptual Information Metric (PIM). We show that
PIM is competitive with supervised metrics on the recent and challenging BAPPS
image quality assessment dataset and outperforms them in predicting the ranking
of image compression methods in CLIC 2020. We also perform qualitative
experiments using the ImageNet-C dataset, and establish that PIM is robust with
respect to architectural details.
