Bridging the Gap Between Saliency Prediction and Image Quality Assessment
This work addresses the problem of understanding human visual perception for researchers in computer vision, but it is incremental as it builds on existing IQA and saliency prediction tasks.
The paper tackles the unclear reasons for deep neural models' success in image quality assessment (IQA) by empirically showing that IQA incorporates knowledge from saliency prediction, and it introduces a novel SACID dataset for saliency-aware compressed images.
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.