基于机器学习和离群熵样本的无参考图像质量评估

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ana Gavrovska, Andreja Samčović, Dragi Dujković
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引用次数: 0

摘要

摘要 由于数字成像技术的进步,越来越多的研究集中在图像质量的评估方法上。因此,对高效的无参照图像质量评估方法的需求与日俱增,因为现实世界中的许多日常应用缺乏无失真图像,即原始版本的图像。本文提出了一种新的无参考图像质量离群熵感知评估方法,用于客观评估现实世界中基于自然场景统计和均值减去及对比度归一化系数的失真图像。系数的分布对无参考图像质量评估非常有用,这里对其特征进行了研究。此外,香农熵和近似熵等熵值也适用于质量评估。最近的研究表明,基于感知的方法与主观评估的相关性存在差异。与其他具有不同失真度的失真图像相比,熵域也显示出类似的异常值。为了解决这些差异,这项工作提出了基于机器学习的离群值熵感知评估模型,以描述影响熵和主观评分的失真多样性。该方法采用了补丁提取,对失真程度进行估计。与现有的基于感知的图像质量评价器相比,该评价器模型利用香农熵和近似熵以及离群点检测,具有高效的优势。使用建议模型获得的结果显示,与人类感知质量评级的相关性有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

No-Reference Image Quality Assessment Based on Machine Learning and Outlier Entropy Samples

No-Reference Image Quality Assessment Based on Machine Learning and Outlier Entropy Samples

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.

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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
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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