This paper outlines an end-to-end optimized lossy image compression framework using diffusion generative models. The approach relies on the transform coding paradigm, where an image is mapped into a latent space for entropy coding and, from there, mapped back to the data space for reconstruction. In contrast to VAE-based neural compression, where the (mean) decoder is a deterministic neural network, our decoder is a conditional diffusion model. Our approach thus introduces an additional ``content'' latent variable on which the reverse diffusion process is conditioned and uses this variable to store information about the image. The remaining ``texture'' variables characterizing the diffusion process are synthesized at decoding time. We show that the model's performance can be tuned toward perceptual metrics of interest. Our extensive experiments involving multiple datasets and image quality assessment metrics show that our approach yields stronger reported FID scores than the GAN-based model, while also yielding competitive performance with VAE-based models in several distortion metrics. Furthermore, training the diffusion with $\mathcal{X}$-parameterization enables high-quality reconstructions in only a handful of decoding steps, greatly affecting the model's practicality. Our code is available at: \url{https://github.com/buggyyang/CDC_compression}

翻译：本文提出了一种基于扩散生成模型的端到端优化有损图像压缩框架。该方法依赖于变换编码范式，将图像映射到潜在空间进行熵编码，再从该空间映射回数据空间进行重建。与基于VAE的神经压缩（其（均值）解码器为确定性神经网络）不同，我们的解码器是一个条件扩散模型。因此，我们的方法引入了一个额外的“内容”潜变量，反向扩散过程以此变量为条件，并利用该变量存储图像信息。描述扩散过程的其余“纹理”变量在解码时合成。我们表明，模型的性能可针对感兴趣的感知指标进行调节。我们在多个数据集和图像质量评估指标上进行了大量实验，结果表明，我们的方法比基于GAN的模型获得了更强的FID分数，同时在若干失真指标上与基于VAE的模型具有竞争性能。此外，使用$\mathcal{X}$参数化训练扩散模型，仅需少量解码步骤即可实现高质量重建，这极大地影响了模型的实用性。我们的代码可在以下网址获取：\url{https://github.com/buggyyang/CDC_compression}