Exploiting neural models for no-reference image quality assessment

We propose an improved algorithm for no-reference image quality assessment (NR-IQA) using the convolutional neural network (CNN) and neural theory based saliency detection. Firstly, we extract non-overlapping patches from the input image. For each patch, we obtain the quality score by CNN network, which consists of seven layers and integrates feature learning and regression into image patch quality estimation. Considering that the patches attracting much attention take significant role in visual perception, an efficient technique based on free energy based neural model is used to detect the saliency map. This saliency map is then applied as a weighting mask to output the quality score of the whole image. Results of experiments show that our algorithm achieves state-of-the-art performance, as compared with the prevailing IQA methods.