Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change

Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users' Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users' perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users' QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.