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

arXiv cs.CV / 4/8/2026

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Key Points

  • The paper studies how diffusion-based image compression performs under bit-level corruption, an aspect often overlooked in rate–distortion–perception optimization.
  • It finds that compressors following the Reverse Channel Coding (RCC) paradigm are substantially more robust to bit flips than classical and other learned codecs.
  • The authors propose a more robust variant of Turbo-DDCM that improves error robustness with only minimal impact on the rate–distortion–perception trade-off.
  • The results suggest RCC-based compression could produce more resilient compressed representations and may lessen the need for conventional error-correcting codes in noisy channels.
  • Overall, the work positions robustness to transmission errors as a key design dimension for next-generation image compression systems.

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.

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