Adaptive Rate Control for Deep Video Compression with Rate-Distortion Prediction
Bowen Gu, Hao Chen, Ming Lu, Jie Yao, Zhan Ma · 2025
Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based$\lambda$-domain rate control scheme for deep video compression, which determines the coding parameter$\lambda$for each to-be-coded frame based on the rate-distortion-$\lambda\ (\mathrm{R}-\mathrm{D}-\lambda)$relationships directly learned from uncompressed frames, achieving high rate control accuracy efficiently without the need for pre-encoding. Moreover, this content-aware scheme is able to mitigate inter-frame quality fluctuations and adapt to abrupt changes in video content. Specifically, we introduce two neural network-based predictors to estimate the relationship between bitrate and$\lambda$, as well as the relationship between distortion and$\lambda$for each frame. Then we determine the coding parameter$\lambda$for each frame to achieve the target bitrate. Experimental results demonstrate that our approach achieves high rate control accuracy at the mini-GOP level with low time overhead and mitigates inter-frame quality fluctuations across video content of varying resolutions.