Contrastive distortion‐level learning‐based no‐reference image‐quality assessment

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 8730-8746 RESEARCH ARTICLE Contrastive distortion-level learning-based no-reference image-quality assessment Xuekai Wei, Xuekai Wei orcid.org/0000-0002-3761-1759 School of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorJing Li, Corresponding Author Jing Li lijing@gzhu.edu.cn orcid.org/0000-0003-0385-8793 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China Correspondence Jing Li, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510002, China. Email: lijing@gzhu.edu.cnSearch for more papers by this authorMingliang Zhou, Mingliang Zhou orcid.org/0000-0002-1874-3641 School of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorXianmin Wang, Xianmin Wang orcid.org/0000-0003-3480-8780 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author Xuekai Wei, Xuekai Wei orcid.org/0000-0002-3761-1759 School of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorJing Li, Corresponding Author Jing Li lijing@gzhu.edu.cn orcid.org/0000-0003-0385-8793 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China Correspondence Jing Li, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510002, China. Email: lijing@gzhu.edu.cnSearch for more papers by this authorMingliang Zhou, Mingliang Zhou orcid.org/0000-0002-1874-3641 School of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorXianmin Wang, Xianmin Wang orcid.org/0000-0003-3480-8780 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author First published: 30 July 2022 https://doi.org/10.1002/int.22965Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract A contrastive distortion-level learning-based no-reference image-quality assessment (NR-IQA) framework is proposed in this study to further effectively model various distortion types with the same or different distortion levels. The proposed method aims to improve the prediction accuracy of NR-IQA. The proposed method consists of three parts: multiscale distortion-level representation learning, single-image NR-IQA, and a representation affinity module, which can reduce NR-IQA computational complexity while maintaining a low-distortion representation of high-distortion inputs. The proposed NR-IQA method aims to extract distributional features of samples in real distorted images and predict ambiguity based on distortion-level learning. Experimental results show that by comparing on many NR-IQA data sets the proposed method can outperform state-of-the-art methods. Volume37, Issue11November 2022Pages 8730-8746 RelatedInformation