Ultrahigh-definition video quality assessment: A new dataset and benchmark
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Published:2024-06
Issue:
Volume:586
Page:127633
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ISSN:0925-2312
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Container-title:Neurocomputing
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language:en
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Short-container-title:Neurocomputing
Author:
Li RuochenORCID, Wang WenminORCID, Hu Huanqiang, Chen Tongbao, Zhao MingluORCID
Reference34 articles.
1. S.A. Golestaneh, S. Dadsetan, K.M. Kitani, No-reference image quality assessment via transformers, relative ranking, and self-consistency, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 1220–1230. 2. W. Sun, X. Min, W. Lu, G. Zhai, A deep learning based no-reference quality assessment model for ugc videos, in: Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 856–865. 3. H. Zhu, L. Li, J. Wu, W. Dong, G. Shi, MetaIQA: Deep meta-learning for no-reference image quality assessment, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14143–14152. 4. Video quality assessment by compact representation of energy in 3D-DCT domain;He;Neurocomputing,2017 5. Video quality assessment with dense features and ranking pooling;Zhang;Neurocomputing,2021
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