Comparative Study of Saliency- and Scanpath-Based Approaches for Patch Selection in Image Quality Assessment

Over the past few years, the development of deep learning-based methods has revolutionised the field of image quality assessment. These methods have shown remarkable success in estimating the quality of 2D images. However, most of these approaches are trained using small patches of the image, with the subjective score of the entire image serving as the target. assuming that all patches have an equal perceptual impact on the image. This assumption is not entirely consistent with our Human Visual System (HVS), which processes different regions of the image to form an overall perception. In this study, we focus on the use of saliency information to estimate image quality. In this context, we explore the use of saliency information to estimate image quality by selecting only the most perceptually relevant patches. Specifically, we evaluate the accuracy of saliency- or scanpath-based patch selection methods for predicting 2D image quality. Our goal is to determine which approach provides the most accurate estimation of image quality and whether the use of saliency information can improve the performance of deep learning-based methods for image quality assessment.