Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis.

State-of-the-art no-reference image quality assessment methods usually learn to evaluate image quality by regression from the human subjective scores of a training set. Their dependence on the regression algorithm and human subjective scores may limit the practical application of such methods. In this paper, we propose a completely blind image quality assessment method that is highly unsupervised and training free. We first use a specific image primitive to analyze the image gray-scale fluctuation and use this result as one of the image quality assessment features. The box-counting method is then used to evaluate the image fractal dimension, and the result is used as the other feature. Finally, the two features are combined together, and a formula is introduced to calculate a comprehensive image quality feature, which is used to measure the image quality. Experimental results on four open databases show that the newly proposed method correlates well with the human subjective judgments of diversely distorted images.