A non-reference perceptual quality metric based on visual attention model for videos

The Human Visual System (HVS) tends to focus on specific regions of viewed images or video frames, this is done effortlessly, instantly and unconsciously. These are called salient regions and form a saliency map, which could be used to improve a number of image and video processing techniques. In this paper, we propose a novel non-reference objective video quality metric based on the saliency map to improve the estimation of the perceived video quality. This metric estimates the degree of blur and blockiness in each video frame from the impaired video only, and uses it with the saliency map to derive a weighting function. The latter is used to modulate the contribution of the pixel differences to the final quality score. The salient regions of the videos are automatically computed using our video saliency model. A psychophysical experiment is conducted to estimate the perceived quality of the impaired videos. The results of this subjective test are compared to the scores obtained with the proposed objective metric. The objective and subjective scores are found to be highly correlated, which shows that our metric correctly estimates the perceived quality of a video.