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arxiv: 2604.05743 · v2 · submitted 2026-04-07 · 💻 cs.CV · cs.AI

On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors

Amit Vaisman , Gal Pomerants , Raz Lapid This is my paper

Pith reviewed 2026-05-10 18:43 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords image compressiondiffusion modelsreverse channel codingbit-flip errorserror robustnessTurbo-DDCMrate-distortion-perception
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The pith

Diffusion-based image compressors built on the Reverse Channel Coding paradigm resist bit-flip errors better than classical and learned codecs.

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

The paper shows that diffusion-based image compression methods relying on the Reverse Channel Coding paradigm keep higher image quality when bit flips corrupt the compressed data. This difference appears in direct comparisons against both traditional codecs and other learned compression approaches. The authors also describe a strengthened version of their Turbo-DDCM compressor that raises this tolerance while leaving the rate-distortion-perception balance nearly unchanged. A sympathetic reader would see this as evidence that certain compression structures can tolerate noisy channels more gracefully.

Core claim

Diffusion-based compressors built on the Reverse Channel Coding paradigm are substantially more robust to bit flips than classical and learned codecs. A modified variant of Turbo-DDCM improves this robustness further while affecting the rate-distortion-perception trade-off only minimally. The results indicate that RCC-based representations can remain usable in error-prone settings without heavy additional protection.

What carries the argument

The Reverse Channel Coding (RCC) paradigm, which arranges the diffusion process so that the decoder can still produce a usable image even after some bits have flipped.

If this is right

  • RCC-based diffusion compressors can produce bitstreams that remain functional after bit flips without requiring separate error-correcting codes.
  • The improved Turbo-DDCM variant delivers higher bit-flip tolerance while keeping rate-distortion-perception performance close to the original.
  • The robustness property is expected to appear in other diffusion compressors that follow the same RCC construction.
  • In environments with frequent bit errors, RCC methods may reduce the total overhead spent on error protection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the advantage persists across varying bit-error rates, designers of wireless image systems could adopt RCC diffusion codecs to lower overall system complexity.
  • Running the same experiments on actual hardware links instead of simulated flips would confirm whether the robustness transfers outside controlled tests.
  • Pairing light error correction with the RCC structure might produce an even stronger combined defense against severe noise.
  • The same RCC principle could be checked for video or point-cloud compression to see whether the robustness benefit generalizes to other media.

Load-bearing premise

The simulated bit-flip model used in the tests matches the dominant errors that occur in the intended real-world transmission or storage environments, and the observed advantage comes mainly from the RCC structure rather than from other implementation choices.

What would settle it

A side-by-side test in which the same images are compressed by RCC and non-RCC methods, transmitted over a real channel that produces measured bit flips, and then decoded, showing no meaningful quality gap between the two families.

Figures

Figures reproduced from arXiv: 2604.05743 by Amit Vaisman, Gal Pomerants, Raz Lapid.

Figure 1
Figure 1. Figure 1: Image Compression Methods Robustness to Bit-Flip Errors: The figure shows reconstructed images from the Kodak24 dataset after transmission through a noisy channel with bit-flip probabilities of 10−4 (first row) and 10−3 (second row). S.C. denotes StableCodec, and T-DDCM denotes Turbo-DDCM. At a bit-flip probability of 10−4 , RCC-based methods (the last three) perform well, whereas the others degrade signif… view at source ↗
Figure 2
Figure 2. Figure 2: Quantitative Results: We compare the bit-flip robustness of several image compression methods, including non-neural, trained neural, and diffusion-based RCC methods, by evaluating distortion (PSNR or LPIPS) and perception (FID) across multiple BER values. Overall, RCC-based methods outperform the others, with Robust Turbo-DDCM demonstrating particularly strong robustness. The dashed vertical line in each s… view at source ↗
Figure 3
Figure 3. Figure 3: Rate-Distortion-Perception Tradeoff: Robust Turbo-DDCM increases the bits per pixel compared to Turbo-DDCM. While it significantly improves robustness, it exhibits inferior rate–distortion–perception performance. off of RCC-based methods in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Results: The presented reconstructions are based on images from the Kodak24 (512 × 512) dataset under a BER of 10−4 . While other methods produce reconstructions that aren’t resemble the original image, RCC-based methods maintains high fidelity [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Results: The presented reconstructions are based on images from the Kodak24 (512 × 512) dataset under a BER of 10−3 . While other methods produce reconstructions that no longer resemble the original image, our method maintains high visual fidelity [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-based compressors built on the Reverse Channel Coding (RCC) paradigm are substantially more robust to bit flips than classical and learned codecs. We further introduce a more robust variant of Turbo-DDCM that significantly improves robustness while only minimally affecting the rate--distortion--perception trade-off. Our findings suggest that RCC-based compression can yield more resilient compressed representations, potentially reducing reliance on error-correcting codes in highly noisy environments.

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 / 3 minor

Summary. The manuscript empirically demonstrates that diffusion-based image compression methods utilizing the Reverse Channel Coding (RCC) paradigm exhibit substantially greater robustness to bit-flip errors in the compressed bitstream compared to both classical codecs and other learned compression approaches. The authors also propose an enhanced variant of the Turbo-DDCM method that further improves this robustness property while maintaining competitive performance on the rate-distortion-perception trade-off.

Significance. If the experimental controls are adequate, this work highlights a potential advantage of RCC-based diffusion compressors for deployment in error-prone environments, such as wireless or satellite communications, where bit errors are common. It could reduce the overhead of error-correcting codes. The introduction of the robust Turbo-DDCM variant adds a practical contribution. However, the significance is tempered by the need to verify that the robustness is indeed due to the RCC construction rather than confounding factors in the implementation.

major comments (2)
  1. [§4 (Experimental Setup)] §4 (Experimental Setup): The comparisons across codecs must ensure that rate-distortion operating points are matched when measuring robustness to bit flips. If RCC-based methods use different latent dimensionalities or quantization schemes, the observed PSNR/SSIM advantages under bit errors could stem from these differences rather than the RCC paradigm itself. Please clarify the matching procedure and report the exact bitrates used for each method in the robustness experiments.
  2. [§5.2 (Bit-flip Error Model)] §5.2 (Bit-flip Error Model): The independent bit-flip model at fixed rates may not capture realistic channel behaviors (e.g., burst errors in wireless links). The paper should include sensitivity analysis or additional experiments with correlated error models to strengthen the claim that RCC provides inherent robustness.
minor comments (3)
  1. [Figure 3] Figure 3: The caption should explicitly state the bit-flip rate used for the displayed examples to aid interpretation.
  2. [§3.1] §3.1: The description of the Turbo-DDCM variant could benefit from a clearer algorithmic outline or pseudocode to distinguish the modifications from the original.
  3. [References] References: Ensure all cited works on diffusion models for compression are up to date, particularly recent advances in RCC applications.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and have revised the manuscript to improve clarity and address the concerns raised.

read point-by-point responses

  1. Referee: The comparisons across codecs must ensure that rate-distortion operating points are matched when measuring robustness to bit flips. If RCC-based methods use different latent dimensionalities or quantization schemes, the observed PSNR/SSIM advantages under bit errors could stem from these differences rather than the RCC paradigm itself. Please clarify the matching procedure and report the exact bitrates used for each method in the robustness experiments.

    Authors: We agree that fair comparison requires matched rate-distortion points. In our experiments, operating points were selected by tuning quantization parameters and latent dimensions for each codec to achieve comparable bitrates (approximately 0.5 bpp on average). To make this explicit, we have added a table in the revised Section 4 that reports the exact bitrates used for every method in the bit-flip robustness experiments, along with a description of the matching procedure. This ensures the robustness gains can be attributed to the RCC construction rather than rate differences. revision: yes

  2. Referee: The independent bit-flip model at fixed rates may not capture realistic channel behaviors (e.g., burst errors in wireless links). The paper should include sensitivity analysis or additional experiments with correlated error models to strengthen the claim that RCC provides inherent robustness.

    Authors: We acknowledge that the independent bit-flip model is a simplification and does not fully capture bursty errors common in wireless channels. Our primary goal was to isolate the effect of random bit flips on the compressed representation. In the revision, we have added a sensitivity analysis in Section 5.2 using a Gilbert-Elliot burst-error model. The results show that the robustness advantage of RCC-based methods, including the improved Turbo-DDCM variant, persists under correlated errors. We believe this addition strengthens the practical relevance of our findings. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical robustness claim with no self-referential derivations

full rationale

The paper's central claim is an empirical observation from bit-flip experiments comparing diffusion-based RCC compressors to baselines. No mathematical derivation chain, equations, or fitted parameters are presented that reduce the robustness result to its own inputs by construction. The abstract frames the finding as experimental evidence rather than a prediction derived from self-defined quantities or self-citations. Any variant of Turbo-DDCM is introduced as an incremental improvement without reducing to a tautology or load-bearing self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, methods, or derivations, so no free parameters, axioms, or invented entities can be identified.

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Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [1]

    NTIRE 2017 challenge on single image super-resolution: Dataset and study

    Eirikur Agustsson and Radu Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. InProceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126–135, 2017. 4

    work page 2017

  2. [2]

    Gener- ative adversarial networks for extreme learned image compression

    Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, and Luc Van Gool. Gener- ative adversarial networks for extreme learned image compression. InProceedings of the IEEE/CVF interna- tional conference on computer vision, pages 221–231,

    work page

  3. [3]

    End-to-end optimized image compression

    Johannes Ballé, Valero Laparra, and Eero P Simoncelli. End-to-end optimized image compression. InInterna- tional Conference on Learning Representations, 2017. 1

    work page 2017

  4. [4]

    Variational image compression with a scale hyperprior

    Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational image compression with a scale hyperprior. InInternational Conference on Learning Representations, 2018. 1, 2

    work page 2018

  5. [5]

    BPG image format

    Fabrice Bellard. BPG image format. https:// bellard.org/bpg/ , 2018. Accessed: 2026-03-

    work page 2018

  6. [6]

    BPG image format, 2018

    Fabrice Bellard. BPG image format, 2018. 4

    work page 2018

  7. [7]

    Sutherland, Michael Ar- bel, and Arthur Gretton

    Mikołaj Bi´nkowski, Dougal J. Sutherland, Michael Ar- bel, and Arthur Gretton. Demystifying MMD GANs. InInternational Conference on Learning Representa- tions, 2018. 3, 4

    work page 2018

  8. [8]

    Rethinking lossy compression: The rate-distortion-perception tradeoff

    Yochai Blau and Tomer Michaeli. Rethinking lossy compression: The rate-distortion-perception tradeoff. InProceedings of the 36th International Conference on Machine Learning, pages 675–685. PMLR, 2019. 1

    work page 2019

  9. [9]

    Deep joint source-channel coding for wireless image transmission.IEEE Transactions on Cognitive Communications and Networking, 5(3):567–579, 2019

    Eirina Bourtsoulatze, David Burth Kurka, and Deniz Gündüz. Deep joint source-channel coding for wireless image transmission.IEEE Transactions on Cognitive Communications and Networking, 5(3):567–579, 2019. 1

    work page 2019

  10. [10]

    Data retention in mlc nand flash memory: Characterization, optimization, and recovery

    Yu Cai, Yixin Luo, Erich F Haratsch, Ken Mai, and Onur Mutlu. Data retention in mlc nand flash memory: Characterization, optimization, and recovery. In2015 IEEE 21st International Symposium on High Perfor- mance Computer Architecture (HPCA), pages 551–563. IEEE, 2015. 1

    work page 2015

  11. [11]

    Towards image compression with perfect realism at ultra-low bitrates

    Marlene Careil, Matthew J Muckley, Jakob Verbeek, and Stéphane Lathuilière. Towards image compression with perfect realism at ultra-low bitrates. InThe Twelfth International Conference on Learning Representations,

    work page

  12. [12]

    Neural joint source- channel coding, 2019

    Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, and Stefano Ermon. Neural joint source- channel coding, 2019. 1, 2

    work page 2019

  13. [13]

    PSC: Posterior sampling-based compression,

    Noam Elata, Tomer Michaeli, and Michael Elad. Psc: Posterior sampling-based compression.arXiv preprint arXiv:2407.09896, 2024. 1, 2

    work page arXiv 2024

  14. [14]

    Kodak lossless true color image suite

    Rich Franzen. Kodak lossless true color image suite. source: http://r0k. us/graphics/kodak, 1999. 4

    work page 1999

  15. [15]

    Elic: Efficient learned im- age compression with unevenly grouped space-channel contextual adaptive coding

    Dailan He, Ziming Yang, Weikun Peng, Rui Ma, Hong- wei Qin, and Yan Wang. Elic: Efficient learned im- age compression with unevenly grouped space-channel contextual adaptive coding. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5718–5727, 2022. 1

    work page 2022

  16. [16]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. InAdvances in Neu- ral Information Processing Systems, pages 6840–6851. Curran Associates, Inc., 2020. 2

    work page 2020

  17. [17]

    Zhihao Hu, Guo Lu, and Dong Xu

    Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer, Luca Versari, George Toderici, and Lucas Theis. High- fidelity image compression with score-based generative models.arXiv preprint arXiv:2305.18231, 2023. 1

    work page arXiv 2023

  18. [18]

    Image compression techniques: A survey in lossless and lossy algorithms.Neurocomputing, 300:44–69,

    Abir Jaafar Hussain, Ali Al-Fayadh, and Naeem Radi. Image compression techniques: A survey in lossless and lossy algorithms.Neurocomputing, 300:44–69,

    work page

  19. [19]

    IEEE. Ieee standard for information technol- ogy—telecommunications and information exchange between systems local and metropolitan area net- works—specific requirements part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications, 2020. 2

    work page 2020

  20. [20]

    Channel-aware ad- versarial attacks against deep learning-based wireless signal classifiers.IEEE Transactions on Wireless Com- munications, 21(6):3868–3880, 2021

    Brian Kim, Yalin E Sagduyu, Kemal Davaslioglu, Tugba Erpek, and Sennur Ulukus. Channel-aware ad- versarial attacks against deep learning-based wireless signal classifiers.IEEE Transactions on Wireless Com- munications, 21(6):3868–3880, 2021. 1

    work page 2021

  21. [21]

    Flipping bits in memory with- out accessing them: an experimental study of dram disturbance errors

    Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, and Onur Mutlu. Flipping bits in memory with- out accessing them: an experimental study of dram disturbance errors. InProceeding of the 41st Annual International Symposium on Computer Architecuture, page 361–372. IEEE Press, 2014. 1

    work page 2014

  22. [22]

    David J. C. MacKay.Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge, UK, 2003. 2

    work page 2003

  23. [23]

    High-fidelity generative image compression.Advances in neural information processing systems, 33:11913–11924, 2020

    Fabian Mentzer, George D Toderici, Michael Tschan- nen, and Eirikur Agustsson. High-fidelity generative image compression.Advances in neural information processing systems, 33:11913–11924, 2020. 1, 2, 4

    work page 2020

  24. [24]

    Joint autoregressive and hierarchical priors for learned image compression.Advances in neural information processing systems, 31, 2018

    David Minnen, Johannes Ballé, and George D Toderici. Joint autoregressive and hierarchical priors for learned image compression.Advances in neural information processing systems, 31, 2018. 1, 2

    work page 2018

  25. [25]

    Improving statistical fidelity for neural image compression with implicit local likelihood models

    Matthew J Muckley, Alaaeldin El-Nouby, Karen Ull- rich, Hervé Jégou, and Jakob Verbeek. Improving statistical fidelity for neural image compression with implicit local likelihood models. InInternational Con- ference on Machine Learning, pages 25426–25443. PMLR, 2023. 4

    work page 2023

  26. [26]

    Deep learning-based image compression for wireless com- munications: impacts on reliability, throughput, and latency.arXiv preprint arXiv:2411.10650, 2024

    Mostafa Naseri, Pooya Ashtari, Mohamed Seif, Eli De Poorter, H Vincent Poor, and Adnan Shahid. Deep learning-based image compression for wireless com- munications: impacts on reliability, throughput, and latency.arXiv preprint arXiv:2411.10650, 2024. 1, 2

    work page arXiv 2024

  27. [27]

    Compressed image generation with denoising diffusion codebook models

    Guy Ohayon, Hila Manor, Tomer Michaeli, and Michael Elad. Compressed image generation with denoising diffusion codebook models. InForty-second International Conference on Machine Learning, 2025. 1, 2, 4

    work page 2025

  28. [28]

    Proakis and Masoud Salehi.Digital Commu- nications

    John G. Proakis and Masoud Salehi.Digital Commu- nications. McGraw-Hill, New York, 5 edition, 2008. 2

    work page 2008

  29. [29]

    Bit- flip attack: Crushing neural network with progressive bit search

    Adnan Siraj Rakin, Zhezhi He, and Deliang Fan. Bit- flip attack: Crushing neural network with progressive bit search. InProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 1211– 1220, 2019. 1

    work page 2019

  30. [30]

    Lossy image compression with foundation diffusion models

    Lucas Relic, Roberto Azevedo, Markus Gross, and Christopher Schroers. Lossy image compression with foundation diffusion models. InEuropean Conference on Computer Vision, pages 303–319. Springer, 2024. 1, 2

    work page 2024

  31. [31]

    Claude E. Shannon. A mathematical theory of commu- nication.ACM SIGMOBILE Mobile Computing and Communications Review, 5(1):3–55, 2001. 2, 3

    work page 2001

  32. [32]

    Algorithms for the communication of samples, 2022

    Lucas Theis and Noureldin Yosri. Algorithms for the communication of samples, 2022. 1

    work page 2022

  33. [33]

    Lossy compression with gaussian diffusion,

    Lucas Theis, Tim Salimans, Matthew D Hoffman, and Fabian Mentzer. Lossy compression with gaussian diffusion.arXiv preprint arXiv:2206.08889, 2022. 4

    work page arXiv 2022

  34. [34]

    Turbo-ddcm: Fast and flexible zero-shot diffusion-based image compression,

    Amit Vaisman, Guy Ohayon, Hila Manor, Michael Elad, and Tomer Michaeli. Turbo-ddcm: Fast and flexible zero-shot diffusion-based image compression,

    work page

  35. [35]

    Lossy compression with pretrained diffusion models

    Jeremy V onderfecht and Feng Liu. Lossy compression with pretrained diffusion models. InThe Thirteenth International Conference on Learning Representations,

    work page

  36. [36]

    Gregory K. Wallace. The JPEG still picture compres- sion standard.Commun. ACM, 34(4):30–44, 1991. 2, 4

    work page 1991

  37. [37]

    Lossy image com- pression with conditional diffusion models.Advances in Neural Information Processing Systems, 36:64971– 64995, 2023

    Ruihan Yang and Stephan Mandt. Lossy image com- pression with conditional diffusion models.Advances in Neural Information Processing Systems, 36:64971– 64995, 2023. 1

    work page 2023

  38. [38]

    Lossy image com- pression with conditional diffusion models.Advances in Neural Information Processing Systems, 36, 2024

    Ruihan Yang and Stephan Mandt. Lossy image com- pression with conditional diffusion models.Advances in Neural Information Processing Systems, 36, 2024. 2

    work page 2024

  39. [39]

    The unreasonable ef- fectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable ef- fectiveness of deep features as a perceptual metric. In CVPR, 2018. 3, 4

    work page 2018

  40. [40]

    Stable- codec: Taming one-step diffusion for extreme image compression

    Tianyu Zhang, Xin Luo, Li Li, and Dong Liu. Stable- codec: Taming one-step diffusion for extreme image compression. InProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision (ICCV), pages 17379–17389, 2025. 4 Appendix A. Experimental Configurations and Additional Results In this section, we first summarize the configurations of all evalu...

    work page 2025