Convolutional Neural Network and Saliency Selection for Blind Image Quality Assessment

In this paper, we propose a degradation-based image quality metric without reference using a Convolutional Neural Network (CNN) model and saliency patch selection. The degradation of the image is first identified and the corresponding saliency map is computed. The saliency map is here used to select patches according to their perceptual relevance, while the degradation identification step allows having a specific CNN model, which permits to improve the performance. A CNN model is then used to estimate the quality of each patch and the overall quality is given by the average of the obtained scores. The proposed method was evaluated through three well-known datasets and was compared to some recent methods.