Studying the added value of visual attention in objective image quality metrics based on eye movement data

Current research on image quality assessment tends to include visual attention in objective metrics to further enhance their performance. A variety of computational models of visual attention are implemented in different metrics, but their accuracy in representing human visual attention is not fully proved yet. Thus, to provide more accurate evidence on whether and to what extent visual attention can be beneficial for objective quality prediction, the use of ¿ground truth¿ visual attention data is highly desired. In this paper, the data of an eye-tracking experiment are integrated in two objective metrics well-known in literature. Experimental results demonstrate that there is indeed a gain in performance including visual attention in objective metrics. The amount of gain in performance tends to depend on the type of objective metric and image distortion.