{"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/resift-reliability-weighted-sift-based-image","title":"ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment","arxiv_id":"1811.06090","date":"2018-11-14","proceeding":null,"authors":["Dogancan Temel","Ghassan AlRegib"],"abstract":"This paper presents a full-reference image quality estimator based on SIFT\ndescriptor matching over reliability-weighted feature maps. Reliability\nassignment includes a smoothing operation, a transformation to perceptual color\ndomain, a local normalization stage, and a spectral residual computation with\nglobal normalization. The proposed method ReSIFT is tested on the LIVE and the\nLIVE Multiply Distorted databases and compared with 11 state-of-the-art\nfull-reference quality estimators. In terms of the Pearson and the Spearman\ncorrelation, ReSIFT is the best performing quality estimator in the overall\ndatabases. Moreover, ReSIFT is the best performing quality estimator in at\nleast one distortion group in compression, noise, and blur category.","url_abs":"http://arxiv.org/abs/1811.06090v1","url_pdf":"http://arxiv.org/pdf/1811.06090v1.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":"resift-reliability-weighted-sift-based-image","repo_url":"https://github.com/olivesgatech/ReSIFT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}