No-Reference Image Quality Assessment Using Texture Information Banks

In this paper, we propose a new no-reference quality assessment method which uses a machine learning technique based on texture analysis. The proposed method compares test images with texture images of a public database. Local Binary Patterns (LBPs) are used as local texture feature descriptors. With a Csiszar-Morimoto divergence measure, the histograms of the LBPs of the test images are compared with the histograms of the LBPs of the database texture images, generating a set of difference measures. These difference measures are used to blindly predict the quality of an image. Experimental results show that the proposed method is fast and has a good quality prediction power, outperforming other no-reference image quality assessment methods.