ADGNet: Attention Discrepancy Guided Deep Neural Network for Blind Image Quality Assessment

This work explores how to efficiently incorporate semantic knowledge for blind image quality assessment and proposes an end-to-end attention discrepancy guided deep neural network for perceptual quality assessment. Our method is established on a multi-task learning framework in which two sub-tasks including semantic recognition and image quality prediction are jointly optimized with a shared feature-extracting branch and independent spatial-attention branch. The discrepancy between semantic-aware attention and quality-aware attention is leveraged to refine the quality predictions. The proposed ADGNet is based on the observation that human visual systems exhibit different mechanisms when viewing images with different amounts of distortion. Such a manner would result in the variation of attention discrepancy between the quality branch and semantic branch, which are therefore employed to enhance the accuracy and generalization ability of our method. We systematically study the major components of our framework, and experimental results on both authentically and synthetically distorted image quality datasets demonstrate the superiority of our model as compared to the state-of-the-art approaches.