No-reference image quality assessment based on global and local content perception

Existing no-reference image quality assessment (NR-IQA) methods mainly focus on designing the low-level features related to image degradation. However, the evaluation of image quality is the human visual perception of image content, involving the integrated analysis of global high-level semantics and local low-level characteristics. From this perspective, we propose a NR-IQA framework based on global and local content perception. We adopt the deep convolutional neural network (DCNN) to extract the semantic feature implied in global image content. The perception of visual quality associated with local content utilizes the visual attention and filtering mechanisms of human visual system. The overall image quality is estimated by combining the semantic and local characteristic features generated from the perceptions. Experimental results on the LIVE IQA database demonstrate that our method is superior to the state-of-the-art NR-IQA algorithms and competitive to the popular full-reference IQA methods. Further experiments on the TID2008 dataset show that the proposed approach is robust for various kinds of distortion types.