# Adaptive Rate Control for Deep Video Compression with Rate-Distortion Prediction

arXiv: https://arxiv.org/abs/2412.18834 · HTML: https://mlsanity.com/paper/2412.18834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2312.06640/) (2023)
- [Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression](https://mlsanity.com/paper/2207.05894/) (2022)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [Bit Allocation using Optimization](https://mlsanity.com/paper/2209.09422/) (2022)
- [Rate Distortion Characteristic Modeling for Neural Image Compression](https://mlsanity.com/paper/2106.12954/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Rate-Quality Model for Learned Video Coding](https://mlsanity.com/paper/2505.02720/) (2025)

## Abstract (arXiv)

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 λ-domain rate control scheme for deep video compression, which determines the coding parameter λ for each to-be-coded frame based on the rate-distortion-λ (R-D-λ) 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 λ, as well as the relationship between distortion and λ for each frame. Then we determine the coding parameter λ 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.