GIP: Generic Image Prior for No Reference Image Quality Assessment

No reference image quality assessment (NR-IQA) has attracted great attention due to the increasing demand in developing perceptually friendly applications. The crucial challenge of this task is how to accurately measure the naturalness of an image. In this paper, we propose a novel parametric image representation which is derived from the generic image prior (GIP). More specifically, we utilize the classic fields of experts model to capture the prior distribution of an image with respect to a random field, which is learned from a great deal of natural images. Then, the parameters in modeling this prior distribution are used as the quality-relevant image feature, which is represented by a simple two-dimension vector. Experimental results show that the proposed method achieves competitive quality prediction accuracy in comparison with the state-of-the-art NR-IQA algorithms at the expense of much less memory usage and computational complexity.