StableCodec: Taming One-Step Diffusion for Extreme Image Compression
Tianyu Zhang, Xin Luo, Li Li, Dong Liu

TL;DR
StableCodec introduces a one-step diffusion-based image compression method that achieves ultra-low bitrate, high fidelity, and real-time inference by combining a deep latent codec, dual-branch structure, and end-to-end optimization.
Contribution
It proposes a novel one-step diffusion approach with a deep latent codec and dual-branch structure for extreme image compression at ultra-low bitrates.
Findings
Outperforms existing methods in FID, KID, DISTS metrics.
Achieves high realism and fidelity at 0.005 bits per pixel.
Maintains inference speed comparable to traditional codecs.
Abstract
Diffusion-based image compression has shown remarkable potential for achieving ultra-low bitrate coding (less than 0.05 bits per pixel) with high realism, by leveraging the generative priors of large pre-trained text-to-image diffusion models. However, current approaches require a large number of denoising steps at the decoder to generate realistic results under extreme bitrate constraints, limiting their application in real-time compression scenarios. Additionally, these methods often sacrifice reconstruction fidelity, as diffusion models typically fail to guarantee pixel-level consistency. To address these challenges, we introduce StableCodec, which enables one-step diffusion for high-fidelity and high-realism extreme image compression with improved coding efficiency. To achieve ultra-low bitrates, we first develop an efficient Deep Compression Latent Codec to transmit a noisy latent…
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