Rate Control for Learned Video Compression
Yanghao Li, Xinyao Chen, Jisheng Li, Jiangtao Wen, Yuxing Han, Shan Liu, Xiaozhong Xu
Abstract
Rate control is a critical part for video compression, especially in bandwidth-limited tasks such as live and broadcast. The newly-rising learned video compression has shown advantageous rate-distortion (RD) performance in previous research, but lack of rate control heavily limits its usage in real coding scenarios. In this work, we present the first rate control scheme tailored for learned video compression. Specifically, we explore the inter-frame dependency of learned video compression and propose a novel R-D-λ model accordingly for efficient rate allocation. Additionally, a staged update algorithm is developed for robust parameter estimation. Experiments on public datasets show that, the proposed rate control scheme achieves low rate error while maintaining equal or even higher RD performance, without introducing coding time overhead.
BibTeX
@inproceedings{icassp2022_ratecontrolforle,
title = {Rate Control for Learned Video Compression},
author = {Yanghao Li and Xinyao Chen and Jisheng Li and Jiangtao Wen and Yuxing Han and Shan Liu and Xiaozhong Xu},
booktitle = {ICASSP 2022},
year = {2022}
}