Manap, R.A., Shao, L. and Frangi, A.F. 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 |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 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. Reproduced in accordance with the publisher's self-archiving policy |
Keywords: | image processing and computer vision; image quality assessment; non-parametric classification and regression |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Dec 2016 16:13 |
Last Modified: | 21 Mar 2018 05:50 |
Published Version: | http://doi.org/10.1109/MMUL.2016.2 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/MMUL.2016.2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109374 |