Blind image quality assessment based on Benford's law

Abstract

The goal of blind image quality assessment (IQA) is to predict the quality of an image as perceived by human observers without using a reference image. The authors explore a new approach which predicts the image quality based on the conformity of the first digit distribution (FDD) of natural images in the transform domain with Benford's law (BL). The conformity is measured by the symmetric Kullback-Leibler divergence. They first show that while in the transform domain the FDD of a natural image conforms with BL well, the FDD of a distorted natural image violates this conformity. They then train a non-linear regression model which maps features derived from the FDD to the subjective evaluation score of an image. The non-linear mapping is trained using Gaussian process regression with a rational quadratic kernel. The selection of this particular non-linear regression tool is based on extensive experiments and evaluations of many regression tools. They conduct experiments to test the proposed technique using five databases. Results demonstrated that its performance is competitive with those state-of-the-art blind IQA algorithms. In particular, the overall performance of the proposed technique is among the best in all algorithms tested.