{"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/sr-sim-a-fast-and-high-performance-iqa-index","title":"SR-SIM: A fast and high performance IQA index based on spectral residual","arxiv_id":null,"date":"2012-09-01","proceeding":"IEEE International Conference on Image Processing 2012 9","authors":["Lin Zhang","Hongyu Li"],"abstract":"Automatic image quality assessment (IQA) attempts to use computational models to measure the image quality in consistency with subjective ratings. In the past decades, dozens of IQA models have been proposed. Though some of them can predict subjective image quality accurately, their computational costs are usually very high. To meet real-time requirements, in this paper, we propose a novel fast and effective IQA index, namely spectral residual based similarity (SR-SIM), based on a specific visual saliency model, spectral residual visual saliency. SR-SIM is designed based on the hypothesis that an image's visual saliency map is closely related to its perceived quality. Extensive experiments conducted on three large-scale IQA datasets indicate that SR-SIM could achieve better prediction performance than the other state-of-the-art IQA indices evaluated. Moreover, SR-SIM can have a quite low computational complexity. The Matlab source code of SR-SIM and the evaluation results are available online at http://sse.tongji.edu.cn/linzhang/IQA/SR-SIM/SR-SIM.htm.Collapse","url_abs":"https://www.semanticscholar.org/paper/SR-SIM%3A-A-fast-and-high-performance-IQA-index-based-Zhang-Li/9f962cbb9c51e5d2827e495cbb30abcf3c41ecc1","url_pdf":"https://www.semanticscholar.org/paper/SR-SIM%3A-A-fast-and-high-performance-IQA-index-based-Zhang-Li/9f962cbb9c51e5d2827e495cbb30abcf3c41ecc1","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":"SR-SIM","rank_in_archive_order":7,"of":10,"metrics":{"PLCC":"0.8027","SRCC":"0.7551"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on-3","task":"Full reference image quality assessment","dataset":"ESPL","model":"SR-SIM","rank_in_archive_order":2,"of":11,"metrics":{"PLCC":"0.8732","SRCC":"0.8802"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on-2","task":"Full reference image quality assessment","dataset":"KADID10K","model":"SR-SIM","rank_in_archive_order":7,"of":11,"metrics":{"SRCC":"0.839"},"uses_additional_data":false},{"leaderboard":"/sota/full-reference-image-quality-assessment-on","task":"Full reference image quality assessment","dataset":"TID2008","model":"SR-SIM","rank_in_archive_order":5,"of":13,"metrics":{"PLCC":"0.8866","SRCC":"0.8913"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality-1","task":"Video Quality Assessment","dataset":"MSU FR VQA Database","model":"SR-SIM","rank_in_archive_order":9,"of":20,"metrics":{"KLCC":"0.7472","SRCC":"0.9014"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}