Nonparametric Quality Assessment of Natural Images

Manap, Redzuan Abdul, Shao, Ling and Frangi, Alejandro (2016) Nonparametric Quality Assessment of Natural Images. IEEE MultiMedia, 23 (4). pp. 22-30. ISSN 1070-986X

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Official URL: http://dx.doi.org/10.1109/MMUL.2016.2

Abstract

In this article, the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features, a two-stage nonparametric NR-IQA framework is proposed. This approach requires no training phase, and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms.

Item Type: Article
Uncontrolled Keywords: data analysis, image processing and computer vision, image quality assessment, nonparametric classification and regression, multimedia, graphics, intelligent systems
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 09 Dec 2016 14:43
Last Modified: 09 Dec 2016 14:43
URI: http://nrl.northumbria.ac.uk/id/eprint/28850

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