HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Lei Lu, Yize Li, Yanzhi Wang, Wei Wang, Wei Jiang

TL;DR
HDCompression introduces a hybrid framework combining diffusion models, VQ-modeling, and LIC to achieve high-fidelity, perceptually pleasing image compression at ultra-low bitrates, outperforming existing methods.
Contribution
This work proposes a novel hybrid image compression method that leverages diffusion models for enhanced fidelity and combines it with VQ-modeling and LIC, improving performance at ultra-low bitrates.
Findings
Outperforms previous LIC, VQ-modeling, and hybrid methods in metrics and visualization.
Enhances fidelity and perceptual quality at ultra-low bitrates.
Uses lightweight diffusion models with simple sampling schedules.
Abstract
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC suffers from severe artifacts due to heavy quantization, while generative VQ modeling gives poor fidelity due to the mismatch between learned generative priors and specific inputs. In this work, we propose Hybrid-Diffusion Image Compression (HDCompression), a dual-stream framework that utilizes both generative VQ-modeling and diffusion models, as well as conventional LIC, to achieve both high fidelity and high perceptual quality. Different from previous hybrid methods that directly use pre-trained LIC models to generate low-quality fidelity-preserving information from heavily quantized latent, we use diffusion models to extract high-quality complementary fidelity information from the ground-truth input, which…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Video Coding and Compression Technologies · Image and Video Quality Assessment
MethodsDiffusion
