Cross-Domain Feature Similarity Guided Blind Image Quality Assessment - Feng, Chenxi; Ye, Long; Zhang, Qin (Frontiers in Neuroscience, 2022)
This work proposes an end-to-end cross-domain feature similarity guided deep neural network for perceptual quality assessment. Our proposed blind image quality assessment approach is based on the observation that feature similarity across different domains (e.g., Semantic Recognition and Quality Prediction) is well correlated with the subjective quality annotations. Such phenomenon is validated by thoroughly analyze the intrinsic interaction between object recognition task and quality prediction task in terms of characteristics of human visual system. Based on the observation, we designed an explicable and self-contained cross-domain feature similarity guided BIQA framework. Experimental results on both authentical and synthetic image quality databases demonstrate the superiority of our approach, as compared to the state-of-the-art models.