One-Step Diffusion-Based Image Compression with Semantic Distillation

• URL: http://arxiv.org/abs/2505.16687v1
• Date: Thu, 22 May 2025 13:54:09 GMT
• Title: One-Step Diffusion-Based Image Compression with Semantic Distillation
• Authors: Naifu Xue, Zhaoyang Jia, Jiahao Li, Bin Li, Yuan Zhang, Yan Lu,
• Abstract summary: OneDC is a One-step Diffusion-based generative image Codec.<n>OneDC achieves perceptual quality even with one-step generation.
• Score: 25.910952778218146
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 40% bitrate reduction and 20x faster decoding compared to prior multi-step diffusion-based codecs. Code will be released later.

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