Toward Extreme Image Compression with One-Step Diffusion and Quantization Semantics
ZHANG Zhouhong1, QIAO Xin1,2, LI Zhiyuan1, AN Ning3,4, KONG He5
1. State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049; 2. Sichuan Digital Economy Industry Development Research Institute, Chengdu 610037; 3. Institute of Mining Artificial Intelligence, Chinese Institute of Coal Science, Beijing 100013; 4. State Key Laboratory of Intelligent Coal Mining and Strata Con-trol, Beijing 100013; 5. School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055
Abstract：Diffusion-based extreme image compression methods exhibit significant performance advantages under extremely low bitrate scenarios. However, existing methods typically decode the image after multiple sampling steps due to their reliance on the step-by-step denoising strategy of diffusion models, resulting in a trade-off between reconstruction fidelity and inference efficiency. To address this issue, a toward extreme image compression method with one-step diffusion and quantization semantics is proposed. A one-step diffusion strategy is designed. It starts from compressed latent features rather than pure noise. High-quality image reconstruction is achieved with only a single sampling step. Moreover, quantized contrastive language-image pretraining(CLIP) features are introduced to replace text as semantic conditions, providing more fine-grained and reliable semantic guidance for reconstruction. Finally, a pixel-level loss is added to the training to alleviate the distribution discrepancy caused by optimization in the latent feature space, further improving reconstruction quality. Extensive experiments demonstrate that the proposed method achieves superior reconstruction quality with only a single sampling step.
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2026, Vol. 39 
