The context effect for blind image quality assessment.

Image quality assessment (IQA) is a process of visuo-cognitive, which is an essential stage in human interaction with the environment. The study of the context effect (Brown and Daniel, 1987) also shows that the evaluation results made by the human vision system (HVS) is related to the contrast between the distorted image and the background environment. However, the existing IQA methods carry out the quality evaluation that only depends on the distorted image itself and ignores the impact of environment to human perception. In this paper, we propose a novel blind image quality assessment(BIQA) based on the context effect. At first, we use a graphical model to describe how the context effect influences human perception of image quality. Based on the established graph, we construct the context relation between the distorted image and the background environment by the X. Han et al. (2015). Then the context features are extracted from the constructed relation, and the quality-related features are extracted by the fine-tuned neural network from the distorted image in pixel-wise. Finally, these features are concatenated to quantify image quality degradations and then regress to quality scores. In addition, the proposed method is adaptive to various deep neural networks. Experimental results show that the proposed method not only has the state-of-art performance on the synthetic distorted images, but also has a great improvement on the authentic distorted images. [ABSTRACT FROM AUTHOR]