Survey of information theory in visual quality assessment

Abstract

We survey information theoretic approaches to solve a variety of visual quality assessment (QA) problems. These approaches are generally built on natural scene statistical models and lead to practical automatic QA algorithms delivering excellent performance in terms of correlation with human judgments of quality. We study all three categories of image QA models: full reference (FR), reduced reference (RR) and no reference (NR) image QA, as well as FR video QA and information weighting strategies for FR image and video QA. We demonstrate the application of information theory in each of these problems. Each of these problems presents its own challenges in the design of information theoretic QA indices leading to different algorithms under different scenarios. In the algorithms, we survey, FR image and video QA algorithms are based on mutual information or conditional Kolmogorov complexities; RR image QA algorithms either use relative entropy or entropic differences, while the NR QA algorithm applies Renyi entropy, and the weighting strategies rely on mutual information. We also discuss various open research questions, particularly in the realm of NR image QA and all classes of video QA.