{"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/no-reference-color-image-quality-assessment","title":"No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality","arxiv_id":"1812.10695","date":"2018-12-27","proceeding":null,"authors":["Xiaoqiao Chen","Qingyi Zhang","Manhui Lin","Guangyi Yang","Chu He"],"abstract":"This paper presents a high-performance general-purpose no-reference (NR)\nimage quality assessment (IQA) method based on image entropy. The image\nfeatures are extracted from two domains. In the spatial domain, the mutual\ninformation between the color channels and the two-dimensional entropy are\ncalculated. In the frequency domain, the two-dimensional entropy and the mutual\ninformation of the filtered sub-band images are computed as the feature set of\nthe input color image. Then, with all the extracted features, the support\nvector classifier (SVC) for distortion classification and support vector\nregression (SVR) are utilized for the quality prediction, to obtain the final\nquality assessment score. The proposed method, which we call entropy-based\nno-reference image quality assessment (ENIQA), can assess the quality of\ndifferent categories of distorted images, and has a low complexity. The\nproposed ENIQA method was assessed on the LIVE and TID2013 databases and showed\na superior performance. The experimental results confirmed that the proposed\nENIQA method has a high consistency of objective and subjective assessment on\ncolor images, which indicates the good overall performance and generalization\nability of ENIQA. The source code is available on github\nhttps://github.com/jacob6/ENIQA.","url_abs":"http://arxiv.org/abs/1812.10695v1","url_pdf":"http://arxiv.org/pdf/1812.10695v1.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":"no-reference-color-image-quality-assessment","repo_url":"https://github.com/jacob6/ENIQA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"no-reference-image-quality-assessment","task_name":"No-Reference 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}