Manap, RA, Shao, L and Frangi, AF orcid.org/0000-0002-2675-528X (2016) Nonparametric Quality Assessment of Natural Images. IEEE MultiMedia, 23 (4). pp. 22-30. ISSN 1070-986X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Feature extraction, Distortion, Prediction algorithms, Image quality, Databases, Algorithm design and analysis, Training; image processing and computer vision, image quality assessment, nonparametric classification and regression, multimedia, graphics, intelligent systems, data analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Sep 2018 13:16 |
Last Modified: | 07 Sep 2018 13:16 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MMUL.2016.2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135032 |