{"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/image-quality-assessment-from-error","title":"Image quality assessment: from error visibility to structural similarity","arxiv_id":null,"date":"2004-04-13","proceeding":"IEEE Transactions on Image Processing 2004 4","authors":["Zhou Wang","A.C. Bovik","H.R. Sheikh; E.P. Simoncelli"],"abstract":"Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.","url_abs":"https://ieeexplore.ieee.org/document/1284395","url_pdf":"https://ece.uwaterloo.ca/~z70wang/publications/ssim.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":[],"tasks":[{"task_slug":"full-reference-image-quality-assessment","task_name":"Full reference image quality assessment"},{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/full-reference-image-quality-assessment-on-2","task":"Full reference image quality assessment","dataset":"KADID10K","model":"SSIM","rank_in_archive_order":11,"of":11,"metrics":{"SRCC":"0.6329"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on","task":"Full reference image quality assessment","dataset":"TID2008","model":"SSIM","rank_in_archive_order":12,"of":13,"metrics":{"PLCC":"0.7732","SRCC":"0.7749"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality-1","task":"Video Quality Assessment","dataset":"MSU FR VQA Database","model":"SSIM","rank_in_archive_order":11,"of":20,"metrics":{"KLCC":"0.7615","PLCC":"0.9253","SRCC":"0.8999"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-sr-qa-dataset","task":"Video Quality Assessment","dataset":"MSU SR-QA Dataset","model":"SSIM","rank_in_archive_order":52,"of":60,"metrics":{"KLCC":"0.17175","PLCC":"0.20670","SROCC":"0.22468","Type":"FR"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}