Blind Image Quality Assessment with Visual Sensitivity Enhanced Dual-Channel Deep Convolutional Neural Network

Abstract

Recent years, various blind image quality assessment (BIQA) methods based on deep neural network have been proposed and achieved excellent performance. Most existing deep BIQA methods learn a regression model from distorted images with corresponding human subjective scores with end-to-end neural networks. However, such schemes ignore the characteristics of human visual system (HVS) since human beings are the ultimate receivers of the images. This paper proposed a dual-channel deep neural architecture for BIQA, which incorporated the visual sensitivity with taken the psychophysical characteristics of human visual system (HVS) into consideration. Furthermore, a new loss function is employed, which penalizes the deep network when the order of prediction scores is different from the ground truth order. The experimental results on two benchmark IQA databases show that the proposed method outperforms the state-of-the-arts.