Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
Anle Ke, Xu Zhang, Tong Chen, Ming Lu, Chao Zhou, Jiawen Gu, Zhan Ma

TL;DR
ResULIC is a novel ultra lowrate image compression framework that leverages semantic residual coding and a compression-aware diffusion model to significantly improve reconstruction quality and coding efficiency.
Contribution
It introduces Semantic Residual Coding and a Compression-aware Diffusion Model, enhancing image compression by better capturing semantic disparities and aligning diffusion processes with bitrate constraints.
Findings
Achieves 80.7% BD-rate saving in LPIPS
Achieves 66.3% BD-rate saving in FID
Outperforms state-of-the-art diffusion-based methods
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsDiffusion
