  Incorporating diffusion models in the image compression domain has the
potential to produce realistic and detailed reconstructions, especially at
extremely low bitrates. Previous methods focus on using diffusion models as
expressive decoders robust to quantization errors in the conditioning signals,
yet achieving competitive results in this manner requires costly training of
the diffusion model and long inference times due to the iterative generative
process. In this work we formulate the removal of quantization error as a
denoising task, using diffusion to recover lost information in the transmitted
image latent. Our approach allows us to perform less than 10\% of the full
diffusion generative process and requires no architectural changes to the
diffusion model, enabling the use of foundation models as a strong prior
without additional fine tuning of the backbone. Our proposed codec outperforms
previous methods in quantitative realism metrics, and we verify that our
reconstructions are qualitatively preferred by end users, even when other
methods use twice the bitrate.
