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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Image Quality Assessment (IQA) is a critical task of computer vision. Most Full-Reference (FR) IQA methods have limitation in the accurate prediction of perceptual qualities of the traditional distorted images and the Generative Adversarial Networks (GANs) based distorted images. To address this issue, we propose a novel method by Unifying Dual-Attention and Siamese Transformer Network (UniDASTN) for FR-IQA. An important contribution is the spatial attention module composed of a Siamese Transformer Network and a feature fusion block. It can focus on significant regions and effectively maps the perceptual differences between the reference and distorted images to a latent distance for distortion evaluation. Another contribution is the dual-attention strategy that exploits channel attention and spatial attention to aggregate features for enhancing distortion sensitivity. In addition, a novel loss function is designed by jointly exploiting Mean Square Error (MSE), bidirectional Kullback\u2013Leibler divergence, and rank order of quality scores. The designed loss function can offer stable training and thus enables the proposed UniDASTN to effectively learn visual perceptual image quality. Extensive experiments on standard IQA databases are conducted to validate the effectiveness of the proposed UniDASTN. The IQA results demonstrate that the proposed UniDASTN outperforms some state-of-the-art FR-IQA methods on the LIVE, CSIQ, TID2013, and PIPAL databases.<\/jats:p>","DOI":"10.1145\/3597434","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T12:17:01Z","timestamp":1684412221000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Unifying Dual-Attention and Siamese Transformer Network for Full-Reference Image Quality Assessment"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3664-1363","authenticated-orcid":false,"given":"Zhenjun","family":"Tang","sequence":"first","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining &amp; Security, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6888-4859","authenticated-orcid":false,"given":"Zhiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining &amp; Security, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5313-6134","authenticated-orcid":false,"given":"Zhixin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining &amp; Security, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3423-1539","authenticated-orcid":false,"given":"Bineng","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining &amp; Security, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3359-117X","authenticated-orcid":false,"given":"Xianquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining &amp; Security, Guangxi Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-3501","authenticated-orcid":false,"given":"Xinpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-022-01003-8"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-019-2757-1"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2003.09.006"},{"issue":"4","key":"e_1_3_1_5_2","article-title":"Perceptual quality assessment of low-light image enhancement","volume":"17","author":"Zhai Guangtao","year":"2021","unstructured":"Guangtao Zhai, Wei Sun, Xiongkuo Min, and Jiantao Zhou. 2021. 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