扩散模型压缩更抗位翻转错误，可减少对纠错码依赖。
On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors
- 基于反向信道编码的扩散压缩更抗位翻转
- 改进版Turbo-DDCM显著提升鲁棒性
- 适合高噪声环境下的图像传输
现代图像压缩方法通常优化率-失真-感知权衡，但对其位级破坏的鲁棒性研究很少。我们发现基于反向信道编码（RCC）范式的扩散压缩器比传统和学习型编解码器更抗位翻转。我们进一步提出一种更鲁棒的Turbo-DDCM变体，在仅轻微影响率-失真-感知权衡的前提下显著提升鲁棒性。结果表明，RCC-based压缩可生成更可靠的压缩表示，有望在高度噪声环境中降低对纠错码的依赖。
原文摘要 · Abstract (English)
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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