{"title":"No-Reference Image Quality Assessment Based on Machine Learning and Outlier Entropy Samples","authors":"Ana Gavrovska, Andreja Samčović, Dragi Dujković","doi":"10.1134/s105466182470007x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A growing research is focusing on approaches for assessing image quality as a result of advancements in digital imaging. Thus, there is an increasing demand for efficient no-reference image quality assessment methods, as many real-world, everyday applications lack distortion-free, i.e., pristine versions of images. This paper presents a new no-reference image quality outlier entropy perception evaluator method for the objective evaluation of real-world distorted images based on natural scene statistics and mean subtracted and contrast normalized coefficients. Distribution of the coefficients is found useful for no-reference image quality assessment, where their characteristics are investigated here. Moreover, entropies such as Shannon and approximate entropies are found suitable for quality estimation. Recent studies show perception-based approaches that demonstrate differences in correlation with subjective assessments. Similar variations are exhibited in entropy domain showing sample outliers compared to other distorted images with different distortion levels. In order to address these variations, this work presents outlier entropy perception evaluator model based on machine learning in order to describe the diversity of distortions affecting entropy and subjective scoring. Patch extraction is employed in the approach, where distortion level is estimated. The evaluatior model is found to be efficient presenting advantages using Shannon and approximate entropies and outlier detection over available perception-based image quality evaluators. The obtained results using proposed model show significant improvements in the correlation with human perceptual quality ratings.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"90 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PATTERN RECOGNITION AND IMAGE ANALYSIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s105466182470007x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A growing research is focusing on approaches for assessing image quality as a result of advancements in digital imaging. Thus, there is an increasing demand for efficient no-reference image quality assessment methods, as many real-world, everyday applications lack distortion-free, i.e., pristine versions of images. This paper presents a new no-reference image quality outlier entropy perception evaluator method for the objective evaluation of real-world distorted images based on natural scene statistics and mean subtracted and contrast normalized coefficients. Distribution of the coefficients is found useful for no-reference image quality assessment, where their characteristics are investigated here. Moreover, entropies such as Shannon and approximate entropies are found suitable for quality estimation. Recent studies show perception-based approaches that demonstrate differences in correlation with subjective assessments. Similar variations are exhibited in entropy domain showing sample outliers compared to other distorted images with different distortion levels. In order to address these variations, this work presents outlier entropy perception evaluator model based on machine learning in order to describe the diversity of distortions affecting entropy and subjective scoring. Patch extraction is employed in the approach, where distortion level is estimated. The evaluatior model is found to be efficient presenting advantages using Shannon and approximate entropies and outlier detection over available perception-based image quality evaluators. The obtained results using proposed model show significant improvements in the correlation with human perceptual quality ratings.
期刊介绍：
The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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