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In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
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Overview of the high efficiency video coding (hevc) standard
G. J. Sullivan, J. R. Ohm, W. J. Han, and T. Wiegand · 2012
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Overview of the high efficiency video coding (hevc) standard
S. B. Yoo, K. Choi, and J. B. Ra · 2014
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Cas-cnn: A deep convolutional neural network for image compression artifact suppression
L. Cavigelli, P. Hager, and L. Benini · 2015
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Compression artifacts reduction by a deep convolutional network
C. Dong, Y. Deng, C. C. Loy, and X. Tang · 2015
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Compression artifacts reduction by a deep convolutional network
J. Jancsary, S. Nowozin, and C. Rother · 2015
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Learning-based joint super-resolution and deblocking for a highly compressed image
L. Kang, C. C. Hsu, B. Zhuang, C. W. Lin, and C. H. Yeh · 2015
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R. Liao, X. Tao, R. Li, Z. Ma, and J. Jia · 2015
Cisco visual networking index: Global mobile data traffic forecast update, 2016-2021 white paper
CVNI · 2017
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A convolutional neural network approach for post-processing in hevc intra coding
Y. Dai, D. Liu, and F. Wu · 2017
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Optimal bit allocation for ctu level rate control in hevc
S. Li, M. Xu, Z. Wang, and X. Sun · 2017
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Robust video super-resolution with learned temporal dynamics
D. Liu, Z. Wang, Y. Fan, X. Liu, Z. Wang, S. Chang, and T. Huang · 2017
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Video frame synthesis using deep voxel flow
Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala · 2017
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Comparison of the coding efficiency of video coding standards including high efficiency video coding (hevc)
J. R. Ohm, G. J. Sullivan, H. Schwarz, T. K. Tan, and T. Wiegand · 2017
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo · 2015
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Building dual-domain representations for compression artifacts reduction
J. Guo and H. Chao · 2016
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Video super-resolution with convolutional neural networks
A. Kappeler, S. Yoo, Q. Dai, and A. K. Katsaggelos · 2016
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Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
X. Mao, C. Shen, and Y.-B. Yang · 2016
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Look, listen and learn-a multimodal lstm for speaker identification
J. S. Ren, Y. Hu, Y.-W. Tai, C. Wang, L. Xu, W. Sun, and Q. Yan · 2016
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Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
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Detail-revealing deep video super-resolution
X. Tao, H. Gao, R. Liao, J. Wang, and J. Jia · 2017
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A novel deep learningbased method of improving coding efficiency from the decoder-end for hevc
T. Wang, M. Chen, and H. Chao · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
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Enhancing hevc compressed videos with a partition-masked convolutional neural network
X. He, Q. Hu, X. Han, X. Zhang, C. Zhang, and W. Lin · 2018
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Versatile video coding-towards the next generation of video compression
J. R. Ohm and G. J. Sullivan · 2018
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Scale-recurrent network for deep image deblurring
X. Tao, H. Gao, Y. Wang, X. Shen, J. Wang, and J. Jia · 2018
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Video inpainting by jointly learning temporal structure and spatial details
C. Wang, H. Huang, X. Han, and J. Wang · 2018
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Multi-frame quality enhancement for compressed video
R. Yang, M. Xu, Z. Wang, and T. Li · 2018
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