Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution

• URL: http://arxiv.org/abs/2505.20984v1
• Date: Tue, 27 May 2025 10:18:24 GMT
• Title: Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
• Authors: Minghao Han, Weiyi You, Jinhua Zhang, Leheng Zhang, Ce Zhu, Shuhang Gu,
• Abstract summary: We propose a novel diffusion-based generative modeling framework tailored for generative image compression.<n>A reverse neural network is trained to reconstruct images by reversing the compression process directly.<n>This approach achieves smooth rate adjustment and photo-realistic reconstructions with only a minimal number of sampling steps.
• Score: 37.60572296105984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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