A Structural Variation Classification Model for Image Quality Assessment

Structural information is critical for image quality assessment (IQA). In this paper, first, we propose a novel model of structural variations in images. The proposed model classifies the types of structural variation within images into four categories: slight deformations, additive impairments, detail losses, and confusing contents. This system of classification applies to most types of structural variations observed in practice. In this model, each pixel from the distorted images is classified according to its structural variation using fuzzy logic based on a set of structural features extracted from the images. Then, a novel IQA method based on these pixel classifications is proposed. This proposed method evaluates the image quality by combining two aspects: the distribution of different structural variations and the degree of structural differences. We test the proposed method using seven public databases. The experimental results indicate that our method is more consistent with the results of the subjective evaluation than were the nine other state-of-the-art IQA methods. The MATLAB source code of our method is available at http://image.ustc.edu.cn/IQA.html.