No Reference Image Quality Assessment by Information Decomposition

Abstract

No reference (NR) image quality assessment (IQA) is to automatically assess image quality as would be perceived by human without reference images. Currently, almost all state-of-the-art NR IQA approaches are trained and tested on the databases of synthetically distorted images. The synthetically distorted images are usually produced by superimposing one or several common distortions on the clean image, but the authentically distorted images are often simultaneously contaminated by several unknown distortions. Therefore, most IQA performances will greatly drop on the authentically distorted images. Recent researches on the human brain demonstrate that the human visual system (HVS) perceives image scenes by predicting the primary information and avoiding residual uncertainty. According to this theory, a new and robust NR IQA approach is proposed in this paper. By the proposed approach, the distorted image is decomposed into the orderly part and disorderly part to be separately processed as its primary information and uncertainty information. Global features of the distorted image are also calculated to describe the overall image contents. Experimental results on the synthetically and authentically image databases demonstrate that the proposed approach makes great progress in IQA performance.