Reduced-Reference Image Quality Assessment Based on Improved Local Binary Pattern

The structure of image consists of two aspects: intensity of structure and distribution of structure. Image distortions that degrade image quality potentially affect both the intensity and distribution of image structure. Yet most structure-based image quality assessment methods focus only on the change of the intensity of structure. In this paper, we propose an improved structure-based image quality assessment method that takes both into account. First, we employ image gradients magnitude to describe the intensity of structure and attempt to explore the distribution of structure with local binary pattern (LBP) and newly designed center-surrounding pixels pattern (CSPP, complementary pattern for LBP). LBP and CSPP features are mapped into a combined histogram weighted by the intensity of structure to represent the image structure. Finally, the change of structure which can gauge image quality is measured by calculating the similarity of the histograms of the reference and distorted images. Support vector regression (SVR) is employed to pool structure features to predict an image quality score. Experimental results on three benchmark databases demonstrate that the proposed structure pattern can effectively represent the intensity and distribution of the structure of the image. The proposed method achieves high consistency with subjective perception with 17 reference values, performing better than the existing methods.