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Reinforced Rate Control for Neural Video Compression via Inter-Frame Rate-Distortion Awareness

Wuyang Cong, Junqi Shi, Lizhong Wang, Weijing Shi, Ming Lu, Hao Chen, Zhan Ma

TL;DR

This work tackles rate control in neural video compression (NVC), where inter-frame dependencies complicate bitrate budgeting and parameter choices. It introduces a CMDP-based reinforced rate control framework with a spatiotemporal state encoder and a joint action space consisting of the Lagrange multiplier $\lambda$ and a down-sampling factor $m$, learned via an enhanced Actor–Critic with distributional twin-critic objectives. A tailored reward shaping strategy and offline RL training enable the policy to optimize long-term rate–distortion performance under bitrate constraints, achieving per-frame decisions that account for reference-induced effects. Empirical results across multiple NVC architectures and datasets show about 1.1–1.2% rate error and up to 13.9–13.98% BD-rate savings across typical GOPs, with strong generalization to unseen content and minimal computational overhead, indicating practical applicability for bandwidth-fluctuating environments.

Abstract

Neural video compression (NVC) has demonstrated superior compression efficiency, yet effective rate control remains a significant challenge due to complex temporal dependencies. Existing rate control schemes typically leverage frame content to capture distortion interactions, overlooking inter-frame rate dependencies arising from shifts in per-frame coding parameters. This often leads to suboptimal bitrate allocation and cascading parameter decisions. To address this, we propose a reinforcement-learning (RL)-based rate control framework that formulates the task as a frame-by-frame sequential decision process. At each frame, an RL agent observes a spatiotemporal state and selects coding parameters to optimize a long-term reward that reflects rate-distortion (R-D) performance and bitrate adherence. Unlike prior methods, our approach jointly determines bitrate allocation and coding parameters in a single step, independent of group of pictures (GOP) structure. Extensive experiments across diverse NVC architectures show that our method reduces the average relative bitrate error to 1.20% and achieves up to 13.45% bitrate savings at typical GOP sizes, outperforming existing approaches. In addition, our framework demonstrates improved robustness to content variation and bandwidth fluctuations with lower coding overhead, making it highly suitable for practical deployment.

Reinforced Rate Control for Neural Video Compression via Inter-Frame Rate-Distortion Awareness

TL;DR

This work tackles rate control in neural video compression (NVC), where inter-frame dependencies complicate bitrate budgeting and parameter choices. It introduces a CMDP-based reinforced rate control framework with a spatiotemporal state encoder and a joint action space consisting of the Lagrange multiplier and a down-sampling factor , learned via an enhanced Actor–Critic with distributional twin-critic objectives. A tailored reward shaping strategy and offline RL training enable the policy to optimize long-term rate–distortion performance under bitrate constraints, achieving per-frame decisions that account for reference-induced effects. Empirical results across multiple NVC architectures and datasets show about 1.1–1.2% rate error and up to 13.9–13.98% BD-rate savings across typical GOPs, with strong generalization to unseen content and minimal computational overhead, indicating practical applicability for bandwidth-fluctuating environments.

Abstract

Neural video compression (NVC) has demonstrated superior compression efficiency, yet effective rate control remains a significant challenge due to complex temporal dependencies. Existing rate control schemes typically leverage frame content to capture distortion interactions, overlooking inter-frame rate dependencies arising from shifts in per-frame coding parameters. This often leads to suboptimal bitrate allocation and cascading parameter decisions. To address this, we propose a reinforcement-learning (RL)-based rate control framework that formulates the task as a frame-by-frame sequential decision process. At each frame, an RL agent observes a spatiotemporal state and selects coding parameters to optimize a long-term reward that reflects rate-distortion (R-D) performance and bitrate adherence. Unlike prior methods, our approach jointly determines bitrate allocation and coding parameters in a single step, independent of group of pictures (GOP) structure. Extensive experiments across diverse NVC architectures show that our method reduces the average relative bitrate error to 1.20% and achieves up to 13.45% bitrate savings at typical GOP sizes, outperforming existing approaches. In addition, our framework demonstrates improved robustness to content variation and bandwidth fluctuations with lower coding overhead, making it highly suitable for practical deployment.
Paper Structure (16 sections, 8 equations, 6 figures, 5 tables)

This paper contains 16 sections, 8 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Neural Video Compression and Rate Control
  4. Reinforcement Learning
  5. Rate Control for NVC
  6. Problem Formulation
  7. The Impact of Inter-frame Dependencies
  8. Reinforced Rate Control Framework
  9. State Modeling
  10. Action Decision
  11. Reward Shaping
  12. Experiments
  13. Experimental Setup
  14. Experimental Results
  15. Ablation Studies
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Rate Control in NVC. (a) It typically involves bitrate allocation and parameter mapping. (b) The effectiveness of rate control is reflected in its ability to accurately meet the target bitrate while incurring minimal quality degradation.
  • Figure 2: Rate Control Process for the 25-th Frame on BasketballDrive. Ignoring inter-frame rate dependencies leads to improper bitrate allocation. As a result, the coding decision still follows the pretrained R–D curve (blue), producing suboptimal parameters (green “Zhang et al.”).
  • Figure 3: Training Pipeline of the Proposed Actor-Critic Network. The entire process consists of three main components, from state to action and then to reward, corresponding to Sec. \ref{['sec:state_modeling']}, \ref{['sec:action_decision']} and \ref{['sec:reward_shaping']}, in sequence.
  • Figure 4: Frame-Level Rate Control. Evaluated with a unified target bitrate 0.10006 BPP on BasketballDrive sequence.
  • Figure 5: Performance Comparison over 360$^{\circ}$ Video. The test sequence is downloaded from https://www.youtube.com/watch?v=4T8yFnHaJtc, with the results of quality degradation above and rate fluctuation below.
  • ...and 1 more figures