Universal blind image quality assessment using contourlet transform and singular-value decomposition.

Most current state-of-the-art blind image quality assessment (IQA) algorithms usually require process training or learning. Here, we have developed a completely blind IQA model that uses features derived from an image's contourlet transform and singular-value decomposition. The model is used to build algorithms that can predict image quality without any training or any prior knowledge of the images or their distortions. The new method consists of three steps: first, the contourlet transform is used on the image to obtain detailed high-frequency structural information from the image; second, the singular values of the just-obtained "structural image" are computed; and finally, two new universal blind IQA indices are constructed utilizing the area and slope of the truncated singular-value curves of the "structural image." Experimental results on three open databases show that the proposed algorithms deliver quality predictions that have high correlations against human subjective judgments and are highly competitive with the state-of-the-art. [ABSTRACT FROM AUTHOR]