用扩散模型直接逆转压缩过程，实现高质量图像重建。
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
- 将压缩过程视为受SDE控制的前向扩散路径，反向网络直接逆推重建。
- 仅需少量采样步数即可生成逼真图像，率失真性能超越现有方法。
- 适合关注图像压缩与生成融合的科研人员和工程师。
尽管学习型图像压缩（LIC）侧重于高效数据传输，生成式图像压缩（GIC）通过引入生成建模，能够生成照片级真实感的重建图像。本文提出一种专为生成式图像压缩设计的新型基于扩散的建模框架。不同于以往间接利用扩散建模的方法，我们重新将压缩过程本身视为由随机微分方程（SDEs）控制的前向扩散路径。训练一个反向神经网络，通过直接逆转压缩过程来重建图像，无需初始化高斯噪声。该方法实现了平滑的码率调节，并在仅需极少采样步骤的情况下获得逼真重建效果。在基准数据集上的大量实验表明，本方法在感知失真、统计保真度及无参考质量评估等多个指标上均优于现有生成式图像压缩方法。
原文摘要 · Abstract (English)
While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produce photo-realistic reconstructed images. In this paper, we propose a novel diffusion-based generative modeling framework tailored for generative image compression. Unlike prior diffusion-based approaches that indirectly exploit diffusion modeling, we reinterpret the compression process itself as a forward diffusion path governed by stochastic differential equations (SDEs). A reverse neural network is trained to reconstruct images by reversing the compression process directly, without requiring Gaussian noise initialization. This approach achieves smooth rate adjustment and photo-realistic reconstructions with only a minimal number of sampling steps. Extensive experiments on benchmark datasets demonstrate that our method outperforms existing generative image compression approaches across a range of metrics, including perceptual distortion, statistical fidelity, and no-reference quality assessments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。