Abstract
Many different parametric models for video quality assessment have been proposed in the past few years. This paper presents a review of nine recent models which cover a wide range of methodologies and have been validated for estimating video quality due to different degradation factors. Each model is briefly described with key algorithms and relevant parametric formulas. The generalization capability of each model to estimate video quality in real-application scenarios is evaluated and compared with other models, using a dataset created with video sequences from practical applications. These video sequences cover a wide range of possible realistic encoding parameters, labeled with mean opinion scores (MOS) via subjective test. The weakness and strength of each model are remarked. Finally, future work towards a more general parametric model that could apply for a wider range of applications is discussed.
Li Song
Professor, IEEE Senior Member
Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.