A novel objective quality assessment method for perceptual video coding in conversational scenarios

Recently, numerous perceptual video coding approaches have been proposed to use face as ROI regions, for improving perceived visual quality of compressed conversational videos. However, there exists no objective metric, specialized for efficiently evaluating the perceived visual quality of compressed conversational videos. This paper thus proposes an efficient objective quality assessment method, namely Gaussian mixture model based PSNR (GMM-PSNR), for conversational videos. First, eye tracking experiments, together with a face extraction technique, were carried out to identify importance of the regions of background, face, and facial features, through eye fixation points. Next, assuming that the distribution of some eye fixation points obeys Gaussian mixture model, an importance weight map is generated by introducing a new term, eye fixation points/pixel(efp/p). Finally, GMM-PSNR is computed by assigning different penalties to the distortion of each pixel in a video frame, according to the generated weight map. The experimental results show the effectiveness of our GMM-PSNR by investigating its correlation with subjective quality on several test video sequences.