Optimal region-of-interest based visual quality assessment

Visual content typically exhibits regions that particularly attract the viewer's attention, usually referred to as regions-of-interest (ROI). In the context of visual quality one may expect that distortions occurring in the ROI are perceived more annoyingly than distortions in the background (BG). This is especially true given that the human visual system is highly space variant in sampling visual signals. However, this phenomenon of visual attention is only seldom taken into account in visual quality metric design. In this paper, we thus provide a framework for incorporation of visual attention into the design of an objective quality metric by means of regionbased segmentation of the image. To support the metric design we conducted subjective experiments to both quantify the subjective quality of a set of distorted images and also to identify ROI in a set of reference images. Multiobjective optimization is then applied to find the optimal weighting of the ROI and BG quality metrics. It is shown that the ROI based metric design allows to increase quality prediction performance of the considered metric and also of two other contemporary quality metrics.