NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
K. De, Masilamani
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引用次数: 2

Abstract

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available. 
基于随机森林多方法融合的多重失真图像无参考质量测量
近年来,图像质量评价一直是图像处理领域的研究热点之一。图像失真可以由各种来源引起,如噪声、模糊、传输通道错误、压缩伪影等。图像失真可能发生在图像采集过程(模糊/噪声),图像压缩(环形和阻塞伪影)或传输过程中。单个图像可能被多个来源扭曲,评估此类图像的质量是一项极具挑战性的任务。在这种情况下,人类视觉系统可以很容易地识别图像质量,但对于计算机算法来说,执行质量评估任务是非常困难的。在本文中,我们提出了一种新的无参考图像质量评估,用于被多种类型的失真损坏的图像。将所提出的技术与最著名的多重失真图像质量评估框架以及现有的标准状态的全参考和无参考图像质量评估技术进行了比较。
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来源期刊
Image Analysis & Stereology
Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED
CiteScore
2.00
自引率
0.00%
发文量
7
审稿时长
>12 weeks
期刊介绍: Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.
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