A Novel Objective Quality Assessment Method for Video Conferencing Coding  

作　　者：Zhenyu Guan Hanzheng Lv Yuan Ma Jingze Zhang 

机构地区：[1]School of Cyber Science and Technology, BeiHang University [2]School of Electronic and Information Engineering, BeiHang University

出　　处：《China Communications》2019年第4期89-104,共16页中国通信（英文版）

摘　　要：Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-tonoise ratio(GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization(EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel(efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-tonoise ratio(GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization(EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel(efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.

关 键 词：VISUAL quality assessment perceptual CONFERENCING VIDEO CODING IMMERSIVE COMMUNICATION 

分 类 号：TN[电子电信]