Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion

• URL: http://arxiv.org/abs/2505.08281v1
• Date: Tue, 13 May 2025 06:51:23 GMT
• Title: Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
• Authors: Anle Ke, Xu Zhang, Tong Chen, Ming Lu, Chao Zhou, Jiawen Gu, Zhan Ma,
• Abstract summary: ResULIC is a residual-guided ultra lowrate image compression system.<n>It incorporates residual signals into both semantic retrieval and the diffusion-based generation process.<n>It achieves superior objective and subjective performance compared to state-of-the-art diffusion-based methods.
• Score: 28.61304513668606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multimodal large model-based image compression frameworks often rely on a fragmented integration of semantic retrieval, latent compression, and generative models, resulting in suboptimal performance in both reconstruction fidelity and coding efficiency. To address these challenges, we propose a residual-guided ultra lowrate image compression named ResULIC, which incorporates residual signals into both semantic retrieval and the diffusion-based generation process. Specifically, we introduce Semantic Residual Coding (SRC) to capture the semantic disparity between the original image and its compressed latent representation. A perceptual fidelity optimizer is further applied for superior reconstruction quality. Additionally, we present the Compression-aware Diffusion Model (CDM), which establishes an optimal alignment between bitrates and diffusion time steps, improving compression-reconstruction synergy. Extensive experiments demonstrate the effectiveness of ResULIC, achieving superior objective and subjective performance compared to state-of-the-art diffusion-based methods with - 80.7%, -66.3% BD-rate saving in terms of LPIPS and FID. Project page is available at https: //njuvision.github.io/ResULIC/.

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