pith. machine review for the scientific record. sign in

arxiv: 2604.20104 · v1 · submitted 2026-04-22 · 💻 cs.MM

Recognition: unknown

Feedback-Driven Rate Control for Learned Video Compression

Chunhua Peng, Hao Zhang, Xuerui Ma, Zhiheng Xu

Pith reviewed 2026-05-09 23:21 UTC · model grok-4.3

classification 💻 cs.MM
keywords learned video compressionrate controlPI/PID controllerfeedback controlbitrate allocationDCVCrate-distortion performance
0
0 comments X

The pith

A PI/PID feedback controller with GRU adjustment tracks target bitrates within 3 percent error in learned video compression.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a feedback-driven rate control system that lets learned video compression models hit specific target bitrates even as video content changes. It starts with a single-model interface that varies the rate-distortion parameter lambda continuously, then applies a log-domain PI/PID controller to correct lambda in real time using the gap between target bitrate and entropy-estimated bitrate. A separate dual-branch GRU module further refines the control signal when strict total bit budgets must be met. Readers would care because most learned compressors today lack reliable online rate control, limiting their use in bandwidth-constrained applications such as live streaming or storage.

Core claim

We propose a feedback-driven rate control framework for learned video compression. First, we build a single-model multi-rate coding interface on top of a DCVC-style framework, enabling continuous bitrate control through the rate-distortion parameter lambda. Then, a log-domain PI/PID closed-loop controller updates lambda online according to the error between the target bitrate and the entropy-estimated bitrate, achieving stable target bitrate tracking. To further improve frame-level bit allocation under budget constraints, we introduce a dual-branch GRU-based adjustment controller that refines the base control signal using budget-state features and causal coding statistics.

What carries the argument

The log-domain PI/PID closed-loop controller that updates the rate-distortion parameter lambda according to the error between target bitrate and entropy-estimated bitrate.

Load-bearing premise

The entropy-estimated bitrate serves as a sufficiently accurate and timely proxy for the actual encoded bitrate so the closed-loop controller stays stable without visible artifacts or instability on diverse content.

What would settle it

Running the controller on videos with rapid scene changes or unusual motion and observing bitrate errors above 5 percent or visible quality artifacts would falsify the stability claim.

Figures

Figures reproduced from arXiv: 2604.20104 by Chunhua Peng, Hao Zhang, Xuerui Ma, Zhiheng Xu.

Figure 1
Figure 1. Figure 1: Overall framework of the proposed feedback-driven rate control method for learned video compression. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the context-dependent path with [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Budget-constrained RD-optimized adjustment controller. The controller takes budget-state features [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rate-distortion comparison of the lambda-modulated single-model baseline on the HEVC Class [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Frame-wise bitrate comparison on two representative sequences under a common target bitrate. For [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Overall rate–distortion comparison on the HEVC Class B/C/D/E and UVG datasets. The compared [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of mini-GOP budget alignment under the proposed budget-constrained adjustment controller. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Subjective quality comparison. From left to right: original frame with the selected region, cropped [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

End-to-end learned video compression has achieved strong rate-distortion performance, but rate control remains underexplored, especially in target-bitrate-driven and budget-constrained scenarios. Existing methods mainly rely on explicit R-D-lambda modeling or feed-forward prediction, which may lack stable online adjustment when video content varies dynamically. We propose a feedback-driven rate control framework for learned video compression. First, we build a single-model multi-rate coding interface on top of a DCVC-style framework, enabling continuous bitrate control through the rate-distortion parameter lambda. Then, a log-domain PI/PID closed-loop controller updates lambda online according to the error between the target bitrate and the entropy-estimated bitrate, achieving stable target bitrate tracking. To further improve frame-level bit allocation under budget constraints, we introduce a dual-branch GRU-based adjustment controller that refines the base control signal using budget-state features and causal coding statistics. Experiments on UVG and HEVC show that the proposed PI/PID controller achieves average bitrate errors of 2.88% and 2.95% on DCVC and DCVC-TCM, respectively. With the proposed adjustment controller, the method further achieves average BD-rate reductions of 5.69% and 4.49%, while reducing the average bitrate errors to 2.13% and 2.24%. These results show that the proposed method provides a practical solution for learned video compression with both controllable bitrate and improved rate-distortion performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces a feedback-driven rate control framework for learned video compression. It builds a single-model multi-rate interface on DCVC-style codecs by varying the rate-distortion parameter lambda, then applies a log-domain PI/PID closed-loop controller that updates lambda according to the error between a target bitrate and the entropy-estimated bitrate. A dual-branch GRU-based adjustment controller is added to refine frame-level allocation under budget constraints using budget-state features and causal statistics. On UVG and HEVC, the PI/PID controller alone yields average bitrate errors of 2.88% and 2.95% on DCVC and DCVC-TCM; adding the adjustment controller further reduces errors to 2.13% and 2.24% while delivering BD-rate savings of 5.69% and 4.49%.

Significance. If the empirical claims hold under proper validation, the work supplies a practical, online mechanism for precise bitrate targeting in learned video codecs without retraining or multiple models. This is relevant for deployment in bandwidth-constrained settings such as streaming, and the reported BD-rate gains under controlled rates indicate that stable feedback control can also improve rate-distortion performance.

major comments (2)
  1. [Experimental evaluation] The headline results (abstract and experimental evaluation) report bitrate errors and BD-rate reductions measured against actual encoded bitrates after arithmetic coding, yet the PI/PID controller is driven exclusively by the entropy-estimated bitrate. No table, figure, or quantitative analysis is provided that measures the discrepancy or correlation between the entropy proxy and the true encoded rate across test sequences or content types; this gap directly undermines the claim that the closed-loop controller achieves the stated tracking accuracy and stability.
  2. [Experiments] The experimental protocol details required to interpret the 2-3% error figures and BD-rate numbers are absent: target bitrate selection method, exact rate points used for BD-rate computation, number of sequences and frames, variance across runs, and the precise baseline codecs or rate-control methods being compared are not described. Without these, the reproducibility of the reported gains cannot be assessed.
minor comments (1)
  1. [Abstract] The abstract states results on 'UVG and HEVC' without naming the specific sequences, resolutions, or frame counts; adding this information would improve clarity and allow direct comparison with prior work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on experimental validation and protocol details. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation and ensure reproducibility.

read point-by-point responses

  1. Referee: [Experimental evaluation] The headline results (abstract and experimental evaluation) report bitrate errors and BD-rate reductions measured against actual encoded bitrates after arithmetic coding, yet the PI/PID controller is driven exclusively by the entropy-estimated bitrate. No table, figure, or quantitative analysis is provided that measures the discrepancy or correlation between the entropy proxy and the true encoded rate across test sequences or content types; this gap directly undermines the claim that the closed-loop controller achieves the stated tracking accuracy and stability.

    Authors: We agree that an explicit analysis of the relationship between the entropy-estimated bitrate (used by the controller) and the actual encoded bitrate (used for reported metrics) is necessary to fully support the tracking claims. In the revised manuscript, we will add a new subsection with quantitative results, including average absolute differences, correlation coefficients, and per-sequence breakdowns across UVG and HEVC content. This will demonstrate the proxy's reliability and address the concern directly. revision: yes

  2. Referee: [Experiments] The experimental protocol details required to interpret the 2-3% error figures and BD-rate numbers are absent: target bitrate selection method, exact rate points used for BD-rate computation, number of sequences and frames, variance across runs, and the precise baseline codecs or rate-control methods being compared are not described. Without these, the reproducibility of the reported gains cannot be assessed.

    Authors: We acknowledge the absence of these details in the current version. The revised manuscript will expand the experimental section to specify: the target bitrate selection approach (uniform sampling over a practical range), the exact rate points for BD-rate calculations, the full list of sequences and frame counts (UVG: 7 sequences; HEVC classes B/C/D/E), any variance measures across runs, and precise baselines (DCVC and DCVC-TCM with fixed lambda, plus any traditional rate-control references). This will enable complete reproducibility. revision: yes

Circularity Check

0 steps flagged

No load-bearing circularity; controller driven by independent external error signal

full rationale

The paper's core contribution is a standard log-domain PI/PID controller plus a GRU adjustment module that updates lambda from the difference between a user-specified target bitrate and the model's entropy-estimated rate. This error signal is external to the final reported metrics (actual encoded file sizes and BD-rate). No equation or claim reduces a performance number to a fitted parameter by construction, nor does any uniqueness theorem or ansatz rest on self-citation. The only self-reference is the expected citation to the DCVC base codec, which supplies the underlying network rather than the control logic itself. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on the DCVC base model and standard control-theory assumptions; no new physical entities are postulated and only one adjustable control variable is introduced.

free parameters (1)
  • lambda
    Rate-distortion tradeoff parameter that is continuously adjusted by the controller rather than fixed at training time.
axioms (1)
  • domain assumption Entropy bitrate estimate is a reliable real-time proxy for actual encoded bitrate
    Invoked as the feedback measurement that drives the PI/PID update.

pith-pipeline@v0.9.0 · 5565 in / 1251 out tokens · 79557 ms · 2026-05-09T23:21:33.637907+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

43 extracted references

  1. [1]

    Gisle Bjontegaard. 2001. Calculation of average PSNR differences between RD-curves.ITU SG16 Doc. VCEG-M33 (2001). 20 Xu et al

    2001

  2. [2]

    Jiancong Chen, Meng Wang, Pingping Zhang, Shurun Wang, and Shiqi Wang. 2023. Sparse-to-dense: High efficiency rate control for end-to-end scale-adaptive video coding.IEEE Transactions on Circuits and Systems for Video Technology 34, 5 (2023), 4027–4039

    2023

  3. [3]

    Zhenghao Chen, Lucas Relic, Roberto Azevedo, Yang Zhang, Markus Gross, Dong Xu, Luping Zhou, and Christopher Schroers. 2023. Neural video compression with spatio-temporal cross-covariance transformers. InProceedings of the 31st ACM International Conference on Multimedia. 8543–8551

    2023

  4. [4]

    Bowen Gu, Hao Chen, Ming Lu, Jie Yao, and Zhan Ma. 2025. Adaptive rate control for deep video compression with rate-distortion prediction. In2025 Data Compression Conference (DCC). IEEE, 33–42

    2025

  5. [5]

    Zhihao Hu, Guo Lu, and Dong Xu. 2021. FVC: A new framework towards deep video compression in feature space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1502–1511

    2021

  6. [6]

    Zhaoyang Jia, Bin Li, Jiahao Li, Wenxuan Xie, Linfeng Qi, Houqiang Li, and Yan Lu. 2025. Towards practical real-time neural video compression. InProceedings of the Computer Vision and Pattern Recognition Conference. 12543–12552

    2025

  7. [7]

    Bin Li, Houqiang Li, Li Li, and Jinlei Zhang. 2014. 𝜆-Domain Rate Control Algorithm for High Efficiency Video Coding. IEEE Transactions on Image Processing23, 9 (2014), 3841–3854

    2014

  8. [8]

    Jiahao Li, Bin Li, and Yan Lu. 2021. Deep contextual video compression.Advances in Neural Information Processing Systems34 (2021), 18114–18125

    2021

  9. [9]

    Jiahao Li, Bin Li, and Yan Lu. 2022. Hybrid spatial-temporal entropy modelling for neural video compression. In Proceedings of the 30th ACM international conference on multimedia. 1503–1511

    2022

  10. [10]

    Jiahao Li, Bin Li, and Yan Lu. 2023. Neural video compression with diverse contexts. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 22616–22626

    2023

  11. [11]

    Meng Li, Yibo Shi, Jing Wang, and Yunqi Huang. 2023. High visual-fidelity learned video compression. InProceedings of the 31st ACM International Conference on Multimedia. 8057–8066

    2023

  12. [12]

    Yanghao Li, Xinyao Chen, Jisheng Li, Jiangtao Wen, Yuxing Han, Shan Liu, and Xiaozhong Xu. 2022. Rate control for learned video compression. InICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2829–2833

    2022

  13. [13]

    Junqi Liao, Yaojun Wu, Chaoyi Lin, Zhipin Deng, Li Li, Dong Liu, and Xiaoyan Sun. 2025. Ehvc: Efficient hierarchical reference and quality structure for neural video coding. InProceedings of the 33rd ACM International Conference on Multimedia. 12083–12091

    2025

  14. [14]

    Shuhong Liao, Chuanmin Jia, Hongfei Fan, Jingwen Yan, and Siwei Ma. 2024. Rate-quality based rate control model for neural video compression. InICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4215–4219

    2024

  15. [15]

    Jianping Lin, Dong Liu, Jie Liang, Houqiang Li, and Feng Wu. 2021. A deeply modulated scheme for variable-rate video compression. In2021 IEEE International Conference on Image Processing (ICIP). IEEE, 3722–3726

    2021

  16. [16]

    Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, and Zhiyong Gao. 2019. Dvc: An end-to-end deep video compression framework. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11006–11015

    2019

  17. [17]

    Siwei Ma, Wen Gao, and Yan Lu. 2005. Rate-distortion analysis for H. 264/AVC video coding and its application to rate control.IEEE transactions on circuits and systems for video technology15, 12 (2005), 1533–1544

    2005

  18. [18]

    Shengbin Meng, Jun Sun, Yizhou Duan, and Zongming Guo. 2016. Adaptive video streaming with optimized bitstream extraction and PID-based quality control.IEEE Transactions on Multimedia18, 6 (2016), 1124–1137

    2016

  19. [19]

    Alexandre Mercat, Marko Viitanen, and Jarno Vanne. 2020. UVG dataset: 50/120fps 4K sequences for video codec analysis and development. InProceedings of the 11th ACM multimedia systems conference. 297–302

    2020

  20. [20]

    Huu-Tai Phung, Zong-Lin Gao, Yi-Chen Yao, Kuan-Wei Ho, Yi-Hsin Chen, Yu-Hsiang Lin, Alessandro Gnutti, and Wen-Hsiao Peng. 2025. MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding. InProceedings of the IEEE/CVF International Conference on Computer Vision. 19649–19658

    2025

  21. [21]

    Christoph Reich, Biplob Debnath, Deep Patel, Tim Prangemeier, Daniel Cremers, and Srimat Chakradhar. 2024. Deep video codec control for vision models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5732–5741

    2024

  22. [22]

    Liquan Shen, Zhi Liu, Zhaoyang Zhang, and Xuli Shi. 2009. Frame-level bit allocation based on incremental PID algorithm and frame complexity estimation.Journal of Visual Communication and Image Representation20, 1 (2009), 28–34

    2009

  23. [23]

    Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, and Yan Lu. 2022. Temporal context mining for learned video compression.IEEE Transactions on Multimedia25 (2022), 7311–7322

    2022

  24. [24]

    Xihua Sheng, Li Li, Dong Liu, and Shiqi Wang. 2025. Bi-directional deep contextual video compression.IEEE Transactions on Multimedia(2025)

    2025

  25. [25]

    Yunhui Shi, Shaopei An, Jin Wang, and Baocai Yin. 2022. Learned Bi-Directional Motion Prediction for Video Compression. InProceedings of the 4th ACM International Conference on Multimedia in Asia. 1–6. Feedback-Driven Rate Control for Learned Video Compression 21

    2022

  26. [26]

    Gary J Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. 2012. Overview of the high efficiency video coding (HEVC) standard.IEEE Transactions on circuits and systems for video technology22, 12 (2012), 1649–1668

    2012

  27. [27]

    Lv Tang and Xinfeng Zhang. 2024. High efficiency deep-learning based video compression.ACM Transactions on Multimedia Computing, Communications and Applications20, 8 (2024), 1–23

    2024

  28. [28]

    Lv Tang, Xinfeng Zhang, and Li Zhang. 2025. UVC: A unified deep video compression framework.ACM Transactions on Multimedia Computing, Communications and Applications21, 3 (2025), 1–23

    2025

  29. [29]

    Shanshe Wang, Siwei Ma, Shiqi Wang, Debin Zhao, and Wen Gao. 2013. Quadratic 𝜌-domain based rate control algorithm for HEVC. In2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 1695–1699

    2013

  30. [30]

    Yiming Wang, Yaojun Wu, Zhaobin Zhang, Qian Huang, Bin Tang, Zhangjing Yang, Kai Zhang, and Li Zhang. 2026. Dual-Scale Transformer with Variable Bitrate Synchronization for Neural Video Compression.ACM Transactions on Multimedia Computing, Communications and Applications(2026)

    2026

  31. [31]

    Thomas Wiegand, Gary J Sullivan, Gisle Bjontegaard, and Ajay Luthra. 2003. Overview of the H. 264/AVC video coding standard.IEEE Transactions on circuits and systems for video technology13, 7 (2003), 560–576

    2003

  32. [32]

    Chi-Wah Wong, Oscar C Au, and Hong-Kwai Lam. 2004. PID-based real-time rate control. In2004 IEEE International Conference on Multimedia and Expo (ICME)(IEEE Cat. No. 04TH8763), Vol. 1. IEEE, 221–224

    2004

  33. [33]

    Yaojun Wu, Chaoyi Lin, Yiming Wang, Semih Esenlik, Zhaobin Zhang, Kai Zhang, and Li Zhang. 2025. Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement. InProceedings of the 33rd ACM International Conference on Multimedia. 12016–12024

    2025

  34. [34]

    Chenming Xu, Meiqin Liu, Chao Yao, Weisi Lin, and Yao Zhao. 2024. IBVC: Interpolation-driven B-frame video compression.Pattern Recognition153 (2024), 110465

    2024

  35. [35]

    Kepeng Xu and Gang He. 2024. Neural Video Compression with Re-Parametrisation Scene Content-Adaptive Network. InProceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited. 32–38

    2024

  36. [36]

    Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. 2019. Video enhancement with task-oriented flow.International Journal of Computer Vision127, 8 (2019), 1106–1125

    2019

  37. [37]

    Jiayu Yang, Chunhui Yang, Fei Xiong, Yongqi Zhai, and Ronggang Wang. 2024. Learned video compression with adaptive temporal prior and decoded motion-aided quality enhancement.ACM Transactions on Multimedia Computing, Communications and Applications20, 8 (2024), 1–21

    2024

  38. [38]

    Jiayu Yang, Yongqi Zhai, Wei Jiang, Chunhui Yang, Feng Gao, and Ronggang Wang. 2024. Adaptive prediction structure for learned video compression.ACM Transactions on Multimedia Computing, Communications and Applications21, 2 (2024), 1–23

    2024

  39. [39]

    Sun Yu and Ishfaq Ahmad. 2002. New rate control algorithm for MPEG-4 video coding. InVisual Communications and Image Processing 2002, Vol. 4671. SPIE, 698–709

    2002

  40. [40]

    Chenhao Zhang and Wei Gao. 2024. Learned rate control for frame-level adaptive neural video compression via dynamic neural network. InEuropean Conference on Computer Vision. Springer, 239–255

    2024

  41. [41]

    Chun Zhang, Heming Sun, and Jiro Katto. 2025. Flavc: Learned video compression with feature level attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 28019–28028

    2025

  42. [42]

    Yiwei Zhang, Guo Lu, Yunuo Chen, Shen Wang, Yibo Shi, Jing Wang, and Li Song. 2023. Neural rate control for learned video compression. InThe Twelfth International Conference on Learning Representations

    2023

  43. [43]

    Yimin Zhou, Yu Sun, Zhidan Feng, and Shixin Sun. 2011. PID-based bit allocation strategy for H. 264/AVC rate control. IEEE Transactions on Circuits and Systems II: Express Briefs58, 3 (2011), 184–188

    2011