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.

翻译：将扩散模型引入图像压缩领域，有望在极低比特率下实现逼真且细节丰富的重建。现有方法主要将扩散模型用作对条件信号量化误差具有鲁棒性的表达性解码器，但这种方式需要昂贵的扩散模型训练成本，且因迭代生成过程导致推理时间较长。本文将量化误差消除问题建模为去噪任务，利用扩散过程恢复传输图像潜变量中丢失的信息。本方法仅需执行完整扩散生成过程的不足10%，且无需对扩散模型架构进行改动，使得基础模型无需额外微调即可作为强先验使用。本编解码器在定量真实性指标上全面超越现有方法，通过用户验证表明，即使其他方法使用双倍比特率，本方法的重建结果在定性上更受终端用户青睐。