{"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/perceptual-error-logarithm-an-efficient-and","title":"Perceptual Error Logarithm: An Efficient and Effective Analytical Method for Full-Reference Image Quality Assessment","arxiv_id":null,"date":"2025-04-25","proceeding":"IEEE Access 2025 4","authors":["SERGIO A. C. BEZERRA","SÉRGIO A. C. BEZERRA JÚNIOR","JOSÉ L. DE S. PIO","JOSÉ R. H. DE CARVALHO","Keiko V. O. Fonseca"],"abstract":"In this paper, we propose a new perceptual analytical method for full-reference image\r\nassessment. Techniques used in multimedia applications often introduce distortions, which can significantly\r\ndegrade image and video quality. To mitigate these distortions, image quality assessment (IQA) methods\r\nhave been employed to enhance these techniques. However, efficiency and effectiveness are difficult to\r\nachieve simultaneously when designing perceptual IQA methods. To face this challenge, we introduced a new\r\nmethod, named the namely Perceptual Error Logarithm (PEL). Initially, luminance and chrominance maps\r\nare generated after the reduction and spatial color conversion applied to the input images. Through the use of\r\nchrominance and luminance maps, simple absolute difference maps are generated, as well as special feature\r\nmaps. The special maps are derived from perceptual feature extractions applied to gradient magnitude maps.\r\nThese maps are generated immediately following the application of local standard deviation filtering to the\r\nluminance maps. Finally, to calculate the PEL score, a logarithmic function is applied to the perceptual error\r\nmap, which combines the simple and special maps, besides adjusts this combination based on the weight\r\nof the superpixel similarity and local energy maps. Experimental results and discussions on eight databases\r\nprove the high level of effectiveness of the proposed method, which presents, according to the application of\r\nevaluation metrics, much more consistency with subjective evaluations than all the nineteen state-of-the-art\r\nIQA methods that were compared. Moreover, PEL is highly efficient because its processing is performed in\r\nreal-time.","url_abs":"https://ieeexplore.ieee.org/document/10965688","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965688","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":"full-reference-image-quality-assessment-2","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-2","task":"Full reference image quality assessment","dataset":"KADID10K","model":"PEL","rank_in_archive_order":4,"of":11,"metrics":{"SRCC":"0.8762"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}