On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors
This addresses the need for more resilient compressed representations in noisy environments, potentially reducing reliance on error-correcting codes, but it is incremental as it builds on existing diffusion-based methods.
The paper tackled the problem of robustness to bit-level corruption in image compression, showing that diffusion-based compressors using Reverse Channel Coding are substantially more robust to bit flips than classical and learned codecs, with a variant improving robustness while minimally affecting rate-distortion-perception trade-offs.
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.