  Over the past few years, deep neural models have made considerable advances
in image quality assessment (IQA). However, the underlying reasons for their
success remain unclear, owing to the complex nature of deep neural networks.
IQA aims to describe how the human visual system (HVS) works and to create its
efficient approximations. On the other hand, Saliency Prediction task aims to
emulate HVS via determining areas of visual interest. Thus, we believe that
saliency plays a crucial role in human perception. In this work, we conduct an
empirical study that reveals the relation between IQA and Saliency Prediction
tasks, demonstrating that the former incorporates knowledge of the latter.
Moreover, we introduce a novel SACID dataset of saliency-aware compressed
images and conduct a large-scale comparison of classic and neural-based IQA
methods. All supplementary code and data will be available at the time of
publication.
