Abstract
General-purpose blind image quality assessment (BIQA) can be widely used in practical applications. However, human opinion scores corresponding to various distorted images are needed in most of BIQAs, which can improve the performance of algorithms but limits their application in practice. In this paper, we propose an opinion-free (OF) BIQA based on statistical characteristics of natural scenes and their logarithmic derivatives in spatial domain. To obtain the statistical characteristics of natural pristine images, we construct a multivariate Gaussian (MVG) model of a collection of patches selected from them by gradient density of each patch. The quality of the test image is computed as the distance between its MVG model and the pristine model. Experimental results show that our method can improve prediction performance by several percentage points compared with the algorithm based on natural scenes.
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