Effective and Efficient Blind Quality Evaluator for Contrast Distorted Images

This paper mainly focuses on developing a blind quality assessment method that can effectively and efficiently evaluate the quality of contrast distorted images without requiring reference information. Through experiments, we discover and validate that the global intensity change is the main characteristic of contrast distorted images and has a close relationship to the perceptual quality. With these observations, two elements are utilized to quantify this characteristic, i.e., the maximum information entropy of intensity values and the Kullback-Leibler (K-L) divergence between the test image's intensity histogram and the prior one based on the statistical experiment over a great number of high-quality images. To be specific, the entropy represents the valuable information of an image and the K-L divergence reflects the change degree of intensity distribution. In view of these, the proposed method is generated by combining these two elements linearly. Extensive experiments on three publicly available databases demonstrate the superiority of the proposed method. More specifically, it is more consistent with subjective evaluation results than the state-of-the-art image quality assessment methods and requires a lower computational complexity.