Accurate objective quality metrics are of great potential benefit to the video industry, as they promise the means to evaluate the performance of acquisition, display, coding and communication systems. Although the area of image quality assessment has attained maturity in recent years, video quality assessment still has a long way to go to before it reaches the levels of success achieved by still image quality metrics. In this paper, we propose a novel quality metric for video sequences, which we call the Video Information Fidelity Criterion (V-IFC), that utilizes motion information in video sequences, which is the main difference in moving from images to video. We previously proposed a model that describes the statistics of natural video sequences and this model is used in the development of V-IFC. This metric is capable of capturing temporal artifacts in video sequences, in addition to spatial distortions. Results are presented that demonstrate the efficacy of our quality metric by comparing model performance against subjective scores on the database developed by the Video Quality Experts Group (VQEG).

In this Keynote Address paper, we review early work on Image and Video Quality Assessment against the backdrop of an interpretation of image perception as a visual communication problem. As a way of explaining our recent work on Video Quality Assessment, we first describe our recent successful advances on QA algorithms for still images, specifically, the Structural SIMilarity (SSIM) Index and the Visual Information Fidelity (VIF) Index. We then describe our efforts towards extending these Image Quality Assessment frameworks to the much more complex problem of Video Quality Assessment. We also discuss our current efforts towards the design and construction of a generic and publicly-available Video Quality Assessment database. 1.