JIST_2020_art00006_Yin-Zhang[71].pdf (2.95 MB)
No-reference image quality assessment based on multi-order gradients statistics
journal contribution
posted on 2023-06-10, 07:03 authored by Yin Zhang, Xuehan Bai, Junhua Yan, Yongqi Xiao, Chris ChatwinChris Chatwin, Rupert YoungRupert Young, Phil BirchPhil BirchA new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.
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Publication status
- Published
File Version
- Published version
Journal
Journal of Imaging Science and TechnologyISSN
1062-3701Publisher
Society for Imaging Science & TechnologyVolume
64Page range
10505-1Department affiliated with
- Engineering and Design Publications
Notes
Reprinted with permission of IS&T: The Society for Imaging Science and Technology sole copyright owners of The Journal of Imaging Science and TechnologyFull text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2023-05-15First Open Access (FOA) Date
2023-05-22First Compliant Deposit (FCD) Date
2023-05-19Usage metrics
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