{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-2018-pirm-challenge-on-perceptual-image","title":"The 2018 PIRM Challenge on Perceptual Image Super-resolution","arxiv_id":"1809.07517","date":"2018-09-20","proceeding":null,"authors":["Yochai Blau","Roey Mechrez","Radu Timofte","Tomer Michaeli","Lihi Zelnik-Manor"],"abstract":"This paper reports on the 2018 PIRM challenge on perceptual super-resolution\n(SR), held in conjunction with the Perceptual Image Restoration and\nManipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR\nchallenges, our evaluation methodology jointly quantifies accuracy and\nperceptual quality, therefore enabling perceptual-driven methods to compete\nalongside algorithms that target PSNR maximization. Twenty-one participating\nteams introduced algorithms which well-improved upon the existing\nstate-of-the-art methods in perceptual SR, as confirmed by a human opinion\nstudy. We also analyze popular image quality measures and draw conclusions\nregarding which of them correlates best with human opinion scores. We conclude\nwith an analysis of the current trends in perceptual SR, as reflected from the\nleading submissions.","url_abs":"http://arxiv.org/abs/1809.07517v3","url_pdf":"http://arxiv.org/pdf/1809.07517v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/alterzero/DBPN-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/alterzero/RBPN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/alterzero/STARnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/idearibosome/tf-perceptual-eusr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/lizatish/My_CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/ryanxingql/image-quality-assessment-toolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/shnhrtkyk/satellite-super-resolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-2018-pirm-challenge-on-perceptual-image","repo_url":"https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"}],"methods":[],"datasets_introduced":[{"slug":"pirm","name":"PIRM","full_name":"Perceptual Image Restoration and Manipulation"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-quality-assessment-on-msu-sr-qa-dataset","task":"Video Quality Assessment","dataset":"MSU SR-QA Dataset","model":"PI","rank_in_archive_order":33,"of":60,"metrics":{"KLCC":"0.39101","PLCC":"0.53178","SROCC":"0.52319","Type":"NR"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}