{"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/gradient-magnitude-similarity-deviation-on","title":"Gradient magnitude similarity deviation on multiple scales for color image quality assessment","arxiv_id":null,"date":"2017-06-19","proceeding":"IEEE International Conference on Acoustics, Speech and Signal Processing 2017 6","authors":["Bo Zhang","Pedro V. Sander","Amine Bermak"],"abstract":"Recently, various image quality assessment (IQA) metrics based on gradient similarity have been developed. In this paper, we extend the work of gradient magnitude similarity deviation (GMSD) and propose a more efficient metric. First, a novel similarity index is proposed, which gives the flexibility to tune the masking parameter to more closely match the human vision system (HVS). Then, we propose a multi-scale GMSD method by incorporating scores of luminance distortion at different scales. Furthermore, a method for measuring chromatic distortions in YIQ color space based on our metric is proposed. The final IQA index, MS-GMSD c , is obtained by combining luminance and chrominance scores. Experimental results on four comprehensive datasets clearly show that, compared with 14 state-of-the-art IQA methods, our method achieves the best performance for both grayscale and chromatic image assessment.","url_abs":"https://ieeexplore.ieee.org/document/7952357","url_pdf":"https://ieeexplore.ieee.org/document/7952357","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":"image-quality-assessment","task_name":"Image Quality Assessment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-quality-assessment-on-msu-fr-vqa","task":"Image Quality Assessment","dataset":"MSU FR VQA Database","model":"MS-GMSD","rank_in_archive_order":5,"of":6,"metrics":{"SRCC":"0.8949"},"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}