{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:25:11Z","timestamp":1766067911637,"version":"build-2065373602"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62302141, 62331003, 62120106009"],"award-info":[{"award-number":["62302141, 62331003, 62120106009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Natural Science Foundation","award":["L223022"],"award-info":[{"award-number":["L223022"]}]},{"name":"Taishan Scholar Project of Shandong Province","award":["tsqn202306079"],"award-info":[{"award-number":["tsqn202306079"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Video super-resolution (VSR) algorithms aim at recovering a temporally consistent high-resolution (HR) video from its corresponding low-resolution (LR) video sequence. Due to the limited bandwidth during video transmission, most available videos on the internet are compressed. Nevertheless, few existing algorithms consider the compression factor in practical applications. In this paper, we propose an enhanced VSR model towards compressed videos, termed as ECVSR, to simultaneously achieve compression artifacts reduction and SR reconstruction end-to-end. ECVSR contains a motion-excited temporal adaption network (METAN) and a multi-frame SR network (SRNet). The METAN takes decoded LR video frames as input and models inter-frame correlations via bidirectional deformable alignment and motion-excited temporal adaption, where temporal differences are calculated as motion prior to excite the motion-sensitive regions of temporal features. In SRNet, cascaded recurrent multi-scale blocks (RMSB) are employed to learn deep spatio-temporal representations from adapted multi-frame features. Then, we build a reconstruction module for spatio-temporal information integration and HR frame reconstruction, which is followed by a detail refinement module for texture and visual quality enhancement. Extensive experimental results on compressed videos demonstrate the superiority of our method for compressed VSR. Code will be available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/lifengcs\/ECVSR\">https:\/\/github.com\/lifengcs\/ECVSR<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3651309","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T12:07:03Z","timestamp":1709726823000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhanced Video Super-Resolution Network towards Compressed Data"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9862-0432","authenticated-orcid":false,"given":"Feng","family":"Li","sequence":"first","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9526-3221","authenticated-orcid":false,"given":"Yixuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3812-8803","authenticated-orcid":false,"given":"Anqi","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3879-8957","authenticated-orcid":false,"given":"Huihui","family":"Bai","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-4008","authenticated-orcid":false,"given":"Runmin","family":"Cong","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8581-9554","authenticated-orcid":false,"given":"Yao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.sigpro.2009.09.002","article-title":"A super-resolution reconstruction algorithm for surveillance images","volume":"30","author":"Zhang L.","year":"2010","unstructured":"L. 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(2019), 8798\u20138807."},{"key":"e_1_3_1_8_2","volume-title":"IEEE Int. Conf. Comput. Vis.","author":"Noh J.","year":"2019","unstructured":"J. Noh, W. Bae, W. Lee, J. Seo, and G. Kim. 2019. Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection. In IEEE Int. Conf. Comput. Vis. (2019), 9725\u20139734."},{"key":"e_1_3_1_9_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Wang L.","year":"2020","unstructured":"L. Wang, D. Li, Y. Zhu, L. Tian, and Y. Shan. 2020. Dual super-resolution learning for semantic segmentation. In IEEE Conf. Comput. Vis. Pattern Recognit. (2020), 3774\u20133783."},{"key":"e_1_3_1_10_2","volume-title":"IEEE Int. Conf. Comput. Vis.","author":"Khani M.","year":"2021","unstructured":"M. Khani, V. Sivaraman, and M. Alizadeh. 2021. Efficient video compression via content-adaptive super-resolution. In IEEE Int. Conf. Comput. Vis. 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(2017), 2848\u20132857."},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","article-title":"Fast and robust multi-frame super resolution","volume":"13","author":"Farsiu S.","year":"2004","unstructured":"S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar. 2004. Fast and robust multi-frame super resolution. IEEE Trans. Image Process. 13, 10 (2004), 1327\u20131344.","journal-title":"IEEE Trans. Image Process."},{"key":"e_1_3_1_23_2","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Ma Z.","year":"2015","unstructured":"Z. Ma, R. Liao, X. Tao, L. Xu, J. Jia, and E. Wu. 2015. Handling motion blur in multi-frame super-resolution. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 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