Temporal Correlation-Based End-to-End Rate Control in DCVC

被引：0
|
作者：
Yang, Zhenglong [1 ]
Deng, Weihao [1 ]
Wang, Guozhong [2 ]
Fan, Tao [2 ]
Luo, Yixi [1 ]
机构：
[1] Shanghai Univ Engn Sci, Sch Urban Rail Transportat, Shanghai 201620, Peoples R China
[2] Shanghai Univ Engn Sci, Artificial Intelligence Ind Res Inst, Shanghai 201620, Peoples R China
来源：
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2024年 / E107卷 / 12期
关键词：
end-to-end rate control; DCVC; convolutional neural network; temporal correlation;
D O I：
10.1587/transinf.2024EDL8041
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recent deep-learning-based video compression models have demonstrated superior performance over traditional codecs. However, few studies have focused on deep learning rate control. In this paper, end-to-end rate control is proposed for deep contextual video compression (DCVC). With the designed two-branch residual-based network, the optimal bit rate ratio is predicted according to the feature correlation of the adjacent frames. Then, the bit rate can be reasonably allocated for every frame by satisfying the temporal feature. To minimize the rate distortion (RD) cost, the optimal .1 of the current frame can be obtained from a two-branch regression-based network using the temporal encoded information. The experimental results show that the achievable BD-rate (PSNR) and BD-rate (SSIM) of the proposed algorithm are -0.84% and-0.35%, respectively, with 2.25% rate control accuracy.
引用
收藏
页码：1550 / 1553
页数：4
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