Cross-Domain Feature Similarity Guided Blind Image Quality Assessment

Frontiers in Neuroscience

Chenxi Feng¹  Qin Zhang²  Long Ye² 

[1] Key Laboratory of Media Audio and Video, Ministry of Education, Communication University of China, Beijing, China;State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China;

关键词: cross-domain feature similarity;    image quality assessment;    deep learning;    transfer learning;    human visual system;   

DOI  :  10.3389/fnins.2021.767977

来源: DOAJ

【 摘 要 】

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 features 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 an object recognition task and a quality prediction task in terms of characteristics of the 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.

【 授权许可】

Unknown