{"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/boosting-in-image-quality-assessment","title":"Boosting in Image Quality Assessment","arxiv_id":"1811.08429","date":"2018-11-21","proceeding":null,"authors":["Dogancan Temel","Ghassan AlRegib"],"abstract":"In this paper, we analyze the effect of boosting in image quality assessment\nthrough multi-method fusion. Existing multi-method studies focus on proposing a\nsingle quality estimator. On the contrary, we investigate the generalizability\nof multi-method fusion as a framework. In addition to support vector machines\nthat are commonly used in the multi-method fusion, we propose using neural\nnetworks in the boosting. To span different types of image quality assessment\nalgorithms, we use quality estimators based on fidelity, perceptually-extended\nfidelity, structural similarity, spectral similarity, color, and learning. In\nthe experiments, we perform k-fold cross validation using the LIVE, the\nmultiply distorted LIVE, and the TID 2013 databases and the performance of\nimage quality assessment algorithms are measured via accuracy-, linearity-, and\nranking-based metrics. Based on the experiments, we show that boosting methods\ngenerally improve the performance of image quality assessment and the level of\nimprovement depends on the type of the boosting algorithm. Our experimental\nresults also indicate that boosting the worst performing quality estimator with\ntwo or more additional methods leads to statistically significant performance\nenhancements independent of the boosting technique and neural network-based\nboosting outperforms support vector machine-based boosting when two or more\nmethods are fused.","url_abs":"http://arxiv.org/abs/1811.08429v1","url_pdf":"http://arxiv.org/pdf/1811.08429v1.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":"boosting-in-image-quality-assessment","repo_url":"https://github.com/olivesgatech/Boosting-in-IQA","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}