{"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/information-content-weighting-for-perceptual","title":"Information Content Weighting for Perceptual Image Quality Assessment","arxiv_id":null,"date":"2011-05-01","proceeding":"IEEE Transactions on Image Processing 2011 5","authors":["Zhou Wang","Qiang Li"],"abstract":"Many state-of-the-art perceptual image quality as-\r\nsessment (IQA) algorithms share a common two-stage structure:\r\nlocal quality/distortion measurement followed by pooling. While\r\nsignificant progress has been made in measuring local image\r\nquality/distortion, the pooling stage is often done in ad-hoc ways,\r\nlacking theoretical principles and reliable computational models.\r\nThis paper aims to test the hypothesis that when viewing natural\r\nimages, the optimal perceptual weights for pooling should be\r\nproportional to local information content, which can be estimated\r\nin units of bit using advanced statistical models of natural images.\r\nOur extensive studies based upon six publicly-available sub-\r\nject-rated image databases concluded with three useful findings.\r\nFirst, information content weighting leads to consistent improve-\r\nment in the performance of IQA algorithms. Second, surprisingly,\r\nwith information content weighting, even the widely criticized\r\npeak signal-to-noise-ratio can be converted to a competitive\r\nperceptual quality measure when compared with state-of-the-art\r\nalgorithms. Third, the best overall performance is achieved by\r\ncombining information content weighting with multiscale struc-\r\ntural similarity measures.","url_abs":"https://ieeexplore.ieee.org/document/5635337","url_pdf":"https://ece.uwaterloo.ca/~z70wang/publications/IWSSIM.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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/full-reference-image-quality-assessment-on-4","task":"Full reference image quality assessment","dataset":"DRIQ","model":"IW-SSIM","rank_in_archive_order":8,"of":10,"metrics":{"PLCC":"0.7155","SRCC":"0.6903"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on-3","task":"Full reference image quality assessment","dataset":"ESPL","model":"IW-SSIM","rank_in_archive_order":7,"of":11,"metrics":{"PLCC":"0.8300","SRCC":"0.8270"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on","task":"Full reference image quality assessment","dataset":"TID2008","model":"IW-SSIM","rank_in_archive_order":9,"of":13,"metrics":{"PLCC":"0.8579","SRCC":"0.8559"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}