Image quality assessment based on structure variance classification

Abstract

In this paper, we find that the structure variance of images could be divided into four classifications, slight deformations, additive impairments, detail losses, and confusing contents, and what's more, for each classification, subjective evaluation is different. According this, we propose a novel image quality assessment (IQA) method based on structure variance classification. The proposed method classifies the structure variance of each patch into one of the four classifications using binary logic and then summarizes the areas of different classifications. To get more comprehensive evaluation, the proposed method also incorporates the measurements of differences between extracted features. Our method is tested on five public databases and compared with seven state-of-art methods. The experimental results demonstrate that our method can achieve higher consistency in relation to the subjective evaluation compared to the state-of-art IQA methods.