One-Step Diffusion-Based Image Compression with Semantic Distillation

TL;DR

This paper introduces OneDC, a one-step diffusion-based image codec that leverages semantic distillation and hyperprior guidance to achieve state-of-the-art perceptual quality, significantly reducing bitrate and decoding time.

Contribution

The work presents a novel one-step diffusion codec integrating semantic guidance and distillation, enabling fast, high-quality image compression without iterative sampling.

Findings

Achieves over 39% bitrate reduction compared to prior methods.

Offers 20x faster decoding speed.

Maintains state-of-the-art perceptual quality.

Abstract

While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the design of a diffusion-based codec and argue that multi-step sampling is not necessary for generative compression. Based on this insight, we propose OneDC, a One-step Diffusion-based generative image Codec -- that integrates a latent compression module with a one-step diffusion generator. Recognizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior as a semantic signal, overcoming the limitations of text prompts in representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowledge from a pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt a hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offering over 39% bitrate reduction and 20x faster decoding compared to prior multi-step diffusion-based codecs. Project: https://onedc-codec.github.io/