4.7 Article

Interlayer Restoration Deep Neural Network for Scalable High Efficiency Video Coding

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2021.3096072

关键词

Scalable high efficiency video coding (SHVC); deep neural network (DNN); signal-to-noise ratio scalability; spatial scalability; coding efficiency

资金

  1. Sichuan Science and Technology Program [2020YFS0307]

向作者/读者索取更多资源

This paper introduces a deep neural network for scalable high efficiency video coding, which improves visual quality and coding efficiency through interlayer restoration. By utilizing reconstructed frames from different layers, the network generates interlayers with higher quality, enhancing the coding efficiency.
This paper applies an interlayer restoration deep neural network (IRDNN) for scalable high efficiency video coding (SHVC) to improve visual quality and coding efficiency. It is the first time to combine deep neural network (DNN) and SHVC. Considering the coding architecture of SHVC, we elaborate a multi-frame and multi-layer neural network to restore the interlayer of SHVC by utilizing both the adjacent reconstructed frames of the base layer (BL) and enhancement layer (EL). Moreover, we analyze the temporal motion relationship of frames in one layer and the compression degradation relationship of frames between different layers, and propose the synergistic mechanism of motion restoration and compression restoration in our IRDNN. The network can generate an interlayer with higher quality serving for the EL coding and thus enhance the coding efficiency. A large-scale and various-quality-degradation dataset is self-made for the task of interlayer restoration of SHVC. The experimental results show that with our implementation on SHVC, the EL Bjontegaard delta bit-rate (BD-BR) reduction is 9.291% and 6.007% in signal-to-noise ratio scalability and spatial scalability, respectively. The code is available at https://github.com/icecherylXuli/IRDNN

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据