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Hierarchical Random Access Coding for Deep Neural Video Compression
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DOI：10.1109/ACCESS.2023.3283277.png)
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摘要
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Recently, neural video compression networks have obtained impressive results. However, previous neural video compression models mostly focus on low-delay configuration with the order of display being the same as the order of coding. In this paper, we propose a hierarchical random access coding approach that exploits bidirectionally temporal redundancy to improve the coding efficiency of existing deep neural video compression models. The proposed framework applies a video frame interpolation network to improve inter-frame prediction. In addition, a hierarchical coding structure is also proposed in this paper. Experimental results show the proposed framework improves the coding efficiency of the base deep neural model by 48.01% with the UVG dataset, 50.96% with the HEVC-class B dataset, and outperforms the previous deep neural video compression networks.
Keyword:
Image coding
Encoding
Video compression
Interpolation
Video coding
Delays
Bit rate
Random access memory
Neural video compression
hierarchical random access coding
video frame interpolation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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