{"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/from-patches-to-pictures-paq-2-piq-mapping","title":"From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality","arxiv_id":"1912.10088","date":"2019-12-20","proceeding":"CVPR 2020 6","authors":["Zhenqiang Ying","Haoran Niu","Praful Gupta","Dhruv Mahajan","Deepti Ghadiyaram","Alan Bovik"],"abstract":"Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily. Unfortunately, popular NR prediction models perform poorly on real-world distorted pictures. To advance progress on this problem, we introduce the largest (by far) subjective picture quality database, containing about 40000 real-world distorted pictures and 120000 patches, on which we collected about 4M human judgments of picture quality. Using these picture and patch quality labels, we built deep region-based architectures that learn to produce state-of-the-art global picture quality predictions as well as useful local picture quality maps. Our innovations include picture quality prediction architectures that produce global-to-local inferences as well as local-to-global inferences (via feedback).","url_abs":"https://arxiv.org/abs/1912.10088v1","url_pdf":"https://arxiv.org/pdf/1912.10088v1.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":"from-patches-to-pictures-paq-2-piq-mapping","repo_url":"https://github.com/baidut/PaQ-2-PiQ","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"from-patches-to-pictures-paq-2-piq-mapping","repo_url":"https://github.com/fastiqa/fastiqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"no-reference-image-quality-assessment","task_name":"No-Reference Image Quality Assessment"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-quality-assessment-on-msu-nr-vqa","task":"Image Quality Assessment","dataset":"MSU NR VQA Database","model":"PaQ-2-PiQ","rank_in_archive_order":7,"of":10,"metrics":{"KLCC":"0.7079","PLCC":"0.8549","SRCC":"0.8705"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"PaQ-2-PiQ","rank_in_archive_order":13,"of":21,"metrics":{"KLCC":"0.7079","PLCC":"0.8549","SRCC":"0.8705","Type":"NR"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-sr-qa-dataset","task":"Video Quality Assessment","dataset":"MSU SR-QA Dataset","model":"PaQ-2-PiQ","rank_in_archive_order":4,"of":60,"metrics":{"KLCC":"0.57753","PLCC":"0.70988","SROCC":"0.71167","Type":"NR"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1912.10088","atlas_url":"https://app.syntology.ai/?focus=1912.10088","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}