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arxiv: 2605.13476 · v1 · pith:K3IB4553new · submitted 2026-05-13 · 💻 cs.CV

Neural Video Compression with Domain Transfer

Tiange Zhang , Rongqun Lin , Xiandong Meng , Haofeng Wang , Xing Tian , Qi Zhang , Siwei Ma This is my paper

Pith reviewed 2026-05-14 20:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords neural video compressiondomain transferlatent adaptationrate-distortion optimizationgeneralization
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The pith

Neural video codecs can close most of their training-test performance gap by adapting only the latent code at inference time.

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

The paper proposes a domain transfer mechanism that adapts the encoded latent representation during inference to reduce the performance loss caused by mismatches between training and test video distributions. This adaptation occurs without any changes to the encoder or decoder parameters. The method pairs the transfer step with a frame-level dynamic rate-distortion adjustment that varies the loss weighting according to observed quality changes. Experiments report bitrate reductions and improved behavior on content outside the training distribution.

Core claim

DCVC-DT achieves up to 6.21% bitrate savings over the baseline DCVC-DC by using a lightweight online domain transfer that adapts the latent representation and a frame-level dynamic RD adjustment, leading to improved generalization and reduced error propagation.

What carries the argument

lightweight online domain transfer (DT) mechanism that dynamically adapts the encoded latent representation during inference without changing encoder or decoder parameters

Load-bearing premise

The lightweight online domain transfer mechanism can reliably bridge the domain gap between training and test distributions by adapting only the latent representation without any change to encoder or decoder parameters.

What would settle it

Apply the method to video sequences drawn from a domain shift larger than those tested in the paper and check whether the reported bitrate savings and generalization gains remain, shrink, or reverse.

Figures

Figures reproduced from arXiv: 2605.13476 by Haofeng Wang, Qi Zhang, Rongqun Lin, Siwei Ma, Tiange Zhang, Xiandong Meng, Xing Tian.

Figure 1
Figure 1. Figure 1: Overall framework of DCVC-DT. We integrate the proposed Online Domain Transfer (DT) mechanism and Dynamic RD Adjustment strategy into [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the proposed Online Latent Refinement and Frame-level Dynamic RD Adjustment modules. During inference, the encoder iteratively [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: R–D performance comparison on HEVC Class C and D datasets [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The first 32 frames of the BasketballPass sequence from the HEVC [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testing data. Such gaps often arise from distributional discrepancies between training and inference data, which may cause noticeable performance degradation when the testing content differs from the training distribution. To tackle this challenge, we propose DCVC-DT, a domain transfer enhanced neural video compression framework. Specifically, we design a lightweight online domain transfer (DT) mechanism that dynamically adapts the encoded latent representation during inference, effectively bridging the domain gap without modifying the encoder or decoder parameters. In addition, we develop a frame-level dynamic RD (Rate and Distortion) adjustment scheme that actively regulates the ratio of R and D in the loss function based on quality fluctuation, thereby improving rate-distortion performance. Extensive experiments demonstrate that DCVC-DT achieves up to 6.21% bitrate savings over the baseline DCVC-DC, while significantly enhancing generalization to unseen testing data and alleviating error propagation. Our code is available at https://github.com/SunnyMass/DCVC-DT.

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

0 major / 3 minor

Summary. The paper proposes DCVC-DT, a neural video compression framework that adds a lightweight online domain transfer (DT) mechanism to adapt the encoded latent representation at inference time (without changing encoder or decoder parameters) and a frame-level dynamic rate-distortion adjustment scheme driven by observed quality fluctuation. It reports up to 6.21% bitrate savings versus the DCVC-DC baseline, improved generalization on unseen test content, and reduced error propagation, with code released.

Significance. If the empirical claims hold on standard benchmarks, the work offers a practical, low-overhead route to closing train/test domain gaps in neural video coding by operating only on latents; the explicit implementation details and code release make the headline result directly falsifiable and potentially useful for deployment scenarios with distribution shift.

minor comments (3)
  1. [Abstract] Abstract: the headline 6.21% figure and generalization claims are stated without naming the test sets, metrics, or number of sequences; adding one sentence with these details would strengthen the summary.
  2. [Section 3.2] The dynamic RD adjustment is described as regulating the R/D ratio based on quality fluctuation, but the precise mapping from fluctuation measure to lambda (or equivalent) is not shown in equation form; a short derivation or pseudocode would clarify reproducibility.
  3. [Section 4] Table or figure presenting the RD curves: confirm that all comparisons use identical GOP structures, intra-periods, and the same set of test sequences (e.g., UVG, HEVC Class B/C) so that the 6.21% savings can be directly verified.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive review and recommendation of minor revision. We appreciate the recognition of the practical benefits of the lightweight online domain transfer mechanism and the frame-level dynamic RD adjustment in DCVC-DT, as well as the value of the code release for reproducibility.

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper's central claims rest on an empirical framework: a lightweight online domain transfer mechanism that adapts only the latent representation during inference (leaving encoder/decoder frozen) plus a frame-level dynamic RD adjustment derived from observed quality fluctuations. No equations or self-citations are presented that reduce the reported 6.21% bitrate savings or generalization improvements to fitted parameters by construction. The mechanism is described with concrete implementation details (adaptation objective, iteration count) and tied to falsifiable RD curves on standard benchmarks, with code released. The derivation chain is self-contained against external evaluation and does not invoke load-bearing self-citations or ansatzes that collapse to the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond standard neural-network training assumptions.

pith-pipeline@v0.9.0 · 5496 in / 1018 out tokens · 41130 ms · 2026-05-14T20:17:29.373470+00:00 · methodology

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Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    Neural video compression with diverse contexts,

    J. Li, B. Li, and Y . Lu, “Neural video compression with diverse contexts,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22 616–22 626

    work page 2023

  2. [2]

    Vqnerv: Vector quantization neural representation for video compression,

    G. Zhang, L. Tang, and X. Zhang, “Vqnerv: Vector quantization neural representation for video compression,” inIEEE International Symposium on Circuits and Systems, 2024

    work page 2024

  3. [3]

    Efvc: Error-propagation-free neural video coding with reversible transform,

    J. Liao, L. Li, D. Liu, and H. Li, “Efvc: Error-propagation-free neural video coding with reversible transform,” inIEEE International Sympo- sium on Circuits and Systems, 2025

    work page 2025

  4. [4]

    Bi-directional deep contextual video compression,

    X. Sheng, L. Li, D. Liu, and S. Wang, “Bi-directional deep contextual video compression,”IEEE Transactions on Multimedia, 2025

    work page 2025

  5. [5]

    L-lbvc: Long- term motion estimation and prediction for learned bi-directional video compression,

    Y . Zhai, L. Tang, W. Jiang, J. Yang, and R. Wang, “L-lbvc: Long- term motion estimation and prediction for learned bi-directional video compression,” inIEEE Data Compression Conference, 2025, pp. 53–62

    work page 2025

  6. [6]

    Emerging advances in learned video compression: Models, systems and beyond,

    C. Jia, F. Ye, S. Ma, W. Gao, H. Sun, and L. Chiariglione, “Emerging advances in learned video compression: Models, systems and beyond,” inInternational Joint Conference on Artificial Intelligence, 2025

    work page 2025

  7. [7]

    Deep video compression with scaled hier- archical bi-directional motion model,

    F. Ye, L. Zhang, and C. Jia, “Deep video compression with scaled hier- archical bi-directional motion model,” inACM International Conference on Multimedia, 2024, pp. 11 244–11 247

    work page 2024

  8. [8]

    Multiple hypotheses based motion compensation for learned video compression,

    R. Lin, M. Wang, P. Zhang, S. Wang, and S. Kwong, “Multiple hypotheses based motion compensation for learned video compression,” Neurocomputing, vol. 548, p. 126396, 2023

    work page 2023

  9. [9]

    Generative coding: Promise and challenges,

    S. Ma, S. Song, B. Chen, Q. Mao, X. Fang, C. Jia, and S. Wang, “Generative coding: Promise and challenges,”APSIPA Transactions on Signal and Information Processing, vol. 14, 2025

    work page 2025

  10. [10]

    Content adaptive based motion alignment framework for learned video compression,

    T. Zhang, X. Meng, and S. Ma, “Content adaptive based motion alignment framework for learned video compression,” inarXiv preprint arXiv:2512.12936, 2025

    work page arXiv 2025

  11. [11]

    L-stec: Learned video compression with long-term spatio-temporal enhanced context,

    T. Zhang, Z. Huang, X. Meng, K. Zhang, Z. Deng, and S. Ma, “L-stec: Learned video compression with long-term spatio-temporal enhanced context,” inarXiv preprint arXiv:2512.12790, 2025

    work page arXiv 2025

  12. [12]

    Learned image compression with large capacity and low redundancy of latent representation,

    X. Meng, S. Zhu, S. Ma, and B. Zeng, “Learned image compression with large capacity and low redundancy of latent representation,” in IEEE International Conference on Image Processing, 2023, pp. 1640– 1644

    work page 2023

  13. [13]

    Ecvc: Exploiting non-local correlations in multiple frames for contextual video compression,

    W. Jiang, J. Li, K. Zhang, and L. Zhang, “Ecvc: Exploiting non-local correlations in multiple frames for contextual video compression,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition Conference, 2025, pp. 7331–7341

    work page 2025

  14. [14]

    Spatial decomposition and temporal fusion based inter prediction for learned video compression,

    X. Sheng, L. Li, D. Liu, and H. Li, “Spatial decomposition and temporal fusion based inter prediction for learned video compression,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 7, pp. 6460–6473, 2024

    work page 2024

  15. [15]

    Hybrid local-global context learning for neural video compression,

    Y . Zhai, J. Yang, W. Jiang, C. Yang, L. Tang, and R. Wang, “Hybrid local-global context learning for neural video compression,” inIEEE Data Compression Conference, 2024, pp. 322–331

    work page 2024

  16. [16]

    Prediction and reference quality adaptation for learned video compression,

    X. Sheng, L. Li, D. Liu, and H. Li, “Prediction and reference quality adaptation for learned video compression,”IEEE Transactions on Image Processing, 2025

    work page 2025

  17. [17]

    Biecvc: Gated diversifica- tion of bidirectional contexts for learned video compression,

    W. Jiang, J. Li, K. Zhang, and L. Zhang, “Biecvc: Gated diversifica- tion of bidirectional contexts for learned video compression,” inACM International Conference on Multimedia, 2025, pp. 7248–7257

    work page 2025

  18. [18]

    Dmvc: Decomposed motion modeling for learned video compression,

    K. Lin, C. Jia, X. Zhang, S. Wang, S. Ma, and W. Gao, “Dmvc: Decomposed motion modeling for learned video compression,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 7, pp. 3502–3515, 2022

    work page 2022

  19. [19]

    Dy- namic temporal reference aggregation for neural video compression,

    S. Liao, K. Feng, Z. Huang, S. Ma, Q. Wang, L. Chen, and C. Jia, “Dy- namic temporal reference aggregation for neural video compression,” in IEEE Data Compression Conference, 2025, pp. 13–22

    work page 2025

  20. [20]

    Neural video com- pression with dynamic temporal context mining,

    S. Liao, K. Feng, C. Jia, Z. Huang, and S. Ma, “Neural video com- pression with dynamic temporal context mining,” inIEEE International Conference on Wireless Communications and Signal Processing, 2024, pp. 1337–1342

    work page 2024

  21. [21]

    Staco: Spatio-temporal adaptive context optimization for neural video compression,

    K. Feng, S. Liao, Z. Huang, C. Jia, Q. Wang, L. Chen, S. Ma, and W. Gao, “Staco: Spatio-temporal adaptive context optimization for neural video compression,” inIEEE Data Compression Conference. IEEE, 2025, pp. 365–365

    work page 2025

  22. [22]

    Rate-quality based rate control model for neural video compression,

    S. Liao, C. Jia, H. Fan, J. Yan, and S. Ma, “Rate-quality based rate control model for neural video compression,” inIEEE International Conference on Acoustics, Speech and Signal Processing, 2024, pp. 4215–4219

    work page 2024

  23. [23]

    Advanced learning-based coding tools for ecm: Intra prediction and in-loop filtering,

    Y . Zhao, J. Fu, Z. Li, Q. Wang, Z. Huang, J. Zhang, C. Jia, and S. Ma, “Advanced learning-based coding tools for ecm: Intra prediction and in-loop filtering,” inIEEE International Symposium on Circuits and Systems, 2025

    work page 2025

  24. [24]

    Chnerv: Condition enhanced hybrid neural representation for videos,

    T. Zhang, H. Wang, X. Meng, K. Zhang, X. Deng, Z. Huang, and S. Ma, “Chnerv: Condition enhanced hybrid neural representation for videos,” inIEEE International Conference on Acoustics, Speech and Signal Processing, 2026, pp. 8377–8381

    work page 2026

  25. [25]

    Overview of the h. 264/avc video coding standard,

    T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the h. 264/avc video coding standard,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560–576, 2003

    work page 2003

  26. [26]

    Overview of the high efficiency video coding (hevc) standard,

    G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the high efficiency video coding (hevc) standard,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649– 1668, 2012

    work page 2012

  27. [27]

    Overview of the versatile video coding (vvc) standard and its applications,

    B. Bross, Y .-K. Wang, Y . Ye, S. Liu, J. Chen, G. J. Sullivan, and J.- R. Ohm, “Overview of the versatile video coding (vvc) standard and its applications,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 3736–3764, 2021

    work page 2021

  28. [28]

    Content adaptive optimization for neural image compression,

    J. Campos, S. Meierhans, A. Djelouah, and C. Schroers, “Content adaptive optimization for neural image compression,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019

    work page 2019

  29. [29]

    Improving inference for neural image compression,

    Y . Yang, R. Bamler, and S. Mandt, “Improving inference for neural image compression,” inAdvances in Neural Information Processing Systems, vol. 33, 2020, pp. 573–584

    work page 2020

  30. [30]

    Overfitting for fun and profit: Instance-adaptive data compression,

    T. van Rozendaal, I. Huijben, and T. S. Cohen, “Overfitting for fun and profit: Instance-adaptive data compression,” inInternational Conference on Learning Representations, 2021

    work page 2021

  31. [31]

    Universal deep image com- pression via content-adaptive optimization with adapters,

    K. Tsubota, H. Akutsu, and K. Aizawa, “Universal deep image com- pression via content-adaptive optimization with adapters,” inIEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 2529– 2538

    work page 2023

  32. [32]

    Content adaptive latents and decoder for neural image compression,

    G. Pan, G. Lu, Z. Hu, and D. Xu, “Content adaptive latents and decoder for neural image compression,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 556–573

    work page 2022

  33. [33]

    Offline and online optical flow enhancement for deep video compression,

    C. Tang, X. Sheng, Z. Li, H. Zhang, L. Li, and D. Liu, “Offline and online optical flow enhancement for deep video compression,” inAAAI Conference on Artificial Intelligence, vol. 38, no. 6, 2024, pp. 5118– 5126

    work page 2024

  34. [34]

    Group-aware parameter-efficient updating for content-adaptive neural video compression,

    Z. Chen, L. Zhou, Z. Hu, and D. Xu, “Group-aware parameter-efficient updating for content-adaptive neural video compression,” inACM Inter- national Conference on Multimedia, 2024, pp. 11 022–11 031

    work page 2024

  35. [35]

    Deep contextual video compression,

    J. Li, B. Li, and Y . Lu, “Deep contextual video compression,” in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 18 114–18 125

    work page 2021

  36. [36]

    Temporal context min- ing for learned video compression,

    X. Sheng, J. Li, B. Li, L. Li, D. Liu, and Y . Lu, “Temporal context min- ing for learned video compression,”IEEE Transactions on Multimedia, vol. 25, pp. 7311–7322, 2022

    work page 2022

  37. [37]

    Hybrid spatial-temporal entropy modelling for neural video compression,

    J. Li, B. Li, and Y . Lu, “Hybrid spatial-temporal entropy modelling for neural video compression,” inACM International Conference on Multimedia, 2022, pp. 1503–1511

    work page 2022

  38. [38]

    Towards practical real-time neural video compression,

    Z. Jia, B. Li, J. Li, W. Xie, L. Qi, H. Li, and Y . Lu, “Towards practical real-time neural video compression,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 12 543–12 552

    work page 2025

  39. [39]

    Neural video compression with feature modulation,

    J. Li, B. Li, and Y . Lu, “Neural video compression with feature modulation,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 26 099–26 108

    work page 2024

  40. [40]

    Content adaptive and error propagation aware deep video compression,

    G. Lu, C. Cai, X. Zhang, L. Chen, W. Ouyang, D. Xu, and Z. Gao, “Content adaptive and error propagation aware deep video compression,” inEuropean Conference on Computer Vision. Springer, 2020, pp. 456– 472

    work page 2020

  41. [41]

    Frame level content adaptiveλfor neural video compression,

    Z. Zuo, J. Liao, X. Song, Z. Liu, H. Zheng, and D. Liu, “Frame level content adaptiveλfor neural video compression,” inIEEE International Conference on Visual Communications and Image Processing, 2024

    work page 2024

  42. [42]

    Content-adaptive infer- ence for state-of-the-art learned video compression,

    A. Bilican, M. A. Yılmaz, and A. M. Tekalp, “Content-adaptive infer- ence for state-of-the-art learned video compression,”IEEE Open Journal of Signal Processing, 2025

    work page 2025

  43. [43]

    Common test conditions and software reference configura- tions,

    F. Bossen, “Common test conditions and software reference configura- tions,” in3rd. JCT-VC Meeting, Guangzhou, CN, October 2010, 2010

    work page 2010

  44. [44]

    Calculation of average psnr differences between rd- curves,

    G. Bjontegaard, “Calculation of average psnr differences between rd- curves,”ITU-T SG16, Doc. VCEG-M33, 2001

    work page 2001