Bridging the Gap Between Saliency Prediction and Image Quality Assessment

arXiv:2405.04997v1 Announce Type: new
Abstract: 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 …

abstract advances arxiv assessment cs.cv eess.iv gap however human image nature networks neural networks prediction quality success type visual