{"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/bless-bio-inspired-low-level-spatiochromatic","title":"BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment","arxiv_id":"1811.07044","date":"2018-11-16","proceeding":null,"authors":["Dogancan Temel","Ghassan AlRegib"],"abstract":"This paper proposes a biologically-inspired low-level\nspatiochromatic-model-based similarity method (BLeSS) to assist full-reference\nimage-quality estimators that originally oversimplify color perception\nprocesses. More specifically, the spatiochromatic model is based on spatial\nfrequency, spatial orientation, and surround contrast effects. The assistant\nsimilarity method is used to complement image-quality estimators based on phase\ncongruency, gradient magnitude, and spectral residual. The effectiveness of\nBLeSS is validated using FSIM, FSIMc and SR-SIM methods on LIVE, Multiply\nDistorted LIVE, and TID 2013 databases. In terms of Spearman correlation, BLeSS\nenhances the performance of all quality estimators in color-based degradations\nand the enhancement is at 100% for both feature- and spectral residual-based\nsimilarity methods. Moreover, BleSS significantly enhances the performance of\nSR-SIM and FSIM in the full TID 2013 database.","url_abs":"http://arxiv.org/abs/1811.07044v1","url_pdf":"http://arxiv.org/pdf/1811.07044v1.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":"bless-bio-inspired-low-level-spatiochromatic","repo_url":"https://github.com/olivesgatech/BLeSS","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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}