No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism-Reference-Cited by-同舟云学术

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No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism

Published:2022-10-05 Issue:24 Volume:26 Page:13449-13465
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Wang Changzhong^ORCID,Lv Xiang,Ding Weiping,Fan Xiaodong

Funder

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07535-5.pdf

Reference67 articles.

1. Anish M, Anush K, Alan Bovik C (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 12:4695–4708

2. Bosse S, Maniry D, Mller K et al (2017) Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Process 27(1):206–219

3. Bosse S, Maniry D, Wiegand T, Samek W (2016) A deep neural network for image quality assessment. In: Proceedings of IEEE international conference on image processing, Phoenix, AZ, USA, Sep. pp 3773–3777

4. Chen D, Wang Y, Gao W (2020) No-reference image quality assessment: an attention driven approach. IEEE Trans Image Process 29(99):6496–6506

5. Cheng Z, Takeuchi M, Katto J (2017) A pre-saliency map based blind image quality assessment via convolutional neural networks. In: Proceedings of IEEE international symposium on multimedia (ISM), pp 77–82

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