CPDINet: Blind image quality assessment via a content perception and distortion inference network
Author:
Affiliation:
1. School of Computer Science Nanjing University of Information Science and Technology Nanjing People's Republic of China
2. School of Information Engineering Nanchang Hangkong University Nanchang People's Republic of China
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12463
Reference60 articles.
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3. Guo H. Bin Y. Hou Y. et al.:Iqma network: Image quality multi‐scale assessment network. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp.443–452.IEEE Piscataway NJ(2021)
4. Shi S. Bai Q. Cao M. Xia W. et al.:Region‐adaptive deformable network for image quality assessment. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp.324–333.IEEE Piscataway NJ(2021)
5. Image quality assessment via spatial‐transformed domains multi‐feature fusion
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