English(EN) Efficient Generative Image Compression with Quantized Latents Refinement

新的GR-GIC方法提高了图像压缩质量和速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一种新颖的生成图像压缩方法GR-GIC，该方法在保持高感知质量的同时实现了快速推理速度。该方法引入了引导精炼（GR）机制，通过结合熵-上下文引导的潜在变量精炼和用于训练正则化的自适应扰动，在低比特率下提高重建质量。为了实现更快的推理速度，GR-GIC采用了重组的投影残差向量量化（P-RVQ），该方法优化了残差更新，以实现更大的并行性和减少顺序计算。 AI

影响这项研究可能带来更高效、更高质量的图像压缩技术，从而惠及需要快速处理和高视觉保真度的应用。

排序理由这是一篇详细介绍生成图像压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hao Cao, Yinhuan Huang, Wenqi Guo, Zhijin Qin, Jungong Han · 2026-10-22 04:00

基于量化潜在表示精炼的高效生成图像压缩

arXiv:2610.29691v1 Announce Type: new Abstract: Generative image compression (GIC) delivers strong perceptual quality at low bitrates. However, GIC methods with strong perceptual quality often rely on pretrained diffusion models with high latency, while recent lightweight GIC met…

本文如何被排名

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基于量化潜在表示精炼的高效生成图像压缩

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