  Traditionally, the vision community has devised algorithms to estimate the
distance between an original image and images that have been subject to
perturbations. Inspiration was usually taken from the human visual perceptual
system and how the system processes different perturbations in order to
replicate to what extent it determines our ability to judge image quality.
While recent works have presented deep neural networks trained to predict human
perceptual quality, very few borrow any intuitions from the human visual
system. To address this, we present PerceptNet, a convolutional neural network
where the architecture has been chosen to reflect the structure and various
stages in the human visual system. We evaluate PerceptNet on various
traditional perception datasets and note strong performance on a number of them
as compared with traditional image quality metrics. We also show that including
a nonlinearity inspired by the human visual system in classical deep neural
networks architectures can increase their ability to judge perceptual
similarity. Compared to similar deep learning methods, the performance is
similar, although our network has a number of parameters that is several orders
of magnitude less.
