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 X-parameterization enables high-quality reconstructions in only a handful of decoding steps, greatly affecting the model's practicality.

翻译：本文提出了一种利用扩散生成模型进行端到端优化的有损图像压缩框架。该方法基于变换编码范式，将图像映射到潜在空间进行熵编码，再从中映射回数据空间进行重建。与基于VAE（变分自编码器）的神经压缩方法（其均值解码器为确定性神经网络）不同，我们的解码器采用条件扩散模型。该方法引入了一个额外的"内容"潜在变量，用于对逆向扩散过程进行条件约束，并利用该变量存储图像信息。表征扩散过程的其余"纹理"变量则在解码时合成。实验表明，模型性能可根据感兴趣的感知指标进行调优。我们在多个数据集和图像质量评估指标上开展的广泛实验显示：与基于GAN的模型相比，本方法在FID分数上取得更优结果；同时在多种失真度量指标上与VAE模型性能相当。此外，采用X参数化训练的扩散模型仅需少量解码步骤即可实现高质量重建，显著提升了模型的实用性。