  Perceptual image quality assessment (IQA) is the task of predicting the
visual quality of an image as perceived by a human observer. Current
state-of-the-art techniques are based on deep representations trained in
discriminative manner. Such representations may ignore visually important
features, if they are not predictive of class labels. Recent generative models
successfully learn low-dimensional representations using auto-encoding and have
been argued to preserve better visual features. Here we leverage existing
auto-encoders and propose VAE-QA, a simple and efficient method for predicting
image quality in the presence of a full-reference. We evaluate our approach on
four standard benchmarks and find that it significantly improves generalization
across datasets, has fewer trainable parameters, a smaller memory footprint and
faster run time.
