Diffusion-based Extreme Image Compression with Compressed Feature Initialization

Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge, Ajmal Saeed Mian

Keywords: extreme image compression, diffusion models, compressed feature initialization, residual diffusion

TL;DR: This paper presents an efficient diffusion-based extreme image compression model that significantly reduces the number of denoising steps required for reconstruction.

Abstract: Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that starts from pure noise, these methods are limited in both fidelity and efficiency. To address these two issues, we present $\textbf{R}$elay $\textbf{R}$esidual $\textbf{D}$iffusion $\textbf{E}$xtreme $\textbf{I}$mage $\textbf{C}$ompression ($\textbf{RDEIC}$), which leverages compressed feature initialization and residual diffusion. Specifically, we first use the compressed latent features of the image with added noise, instead of pure noise, as the starting point to eliminate the unnecessary initial stages of the denoising process. Second, we design a novel relay residual diffusion that reconstructs the raw image by iteratively removing the added noise and the residual between the compressed and target latent features. Notably, our relay residual diffusion network seamlessly integrates pre-trained stable diffusion to leverage its robust generative capability for high-quality reconstruction. Third, we propose a fixed-step fine-tuning strategy to eliminate the discrepancy between the training and inference phases, further improving the reconstruction quality. Extensive experiments demonstrate that the proposed RDEIC achieves state-of-the-art visual quality and outperforms existing diffusion-based extreme image compression methods in both fidelity and efficiency. The source code and pre-trained models will be released.

Primary Area: generative models

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Submission Number: 2931