English(EN) FLM: Frequency-Aware Language Models for Generative Image Compression

新型语言模型推动生成式图像压缩发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了FLM，这是一种新颖的频率感知语言模型，专为生成式图像压缩而设计。该模型将图像转换为离散的DCT系数序列，然后FLM利用这些序列自回归地预测用于算术编码的token概率。这种方法允许确定性重建，同时显著提高压缩效率，尤其是在低比特率下。实验表明，FLM的性能优于现有方法，在标准数据集上实现了显著的BD-PSNR增益，并通过保持语义保真度和减少块状伪影提供了更好的定性结果。 AI

影响该模型有望实现更高效的图像存储和传输，尤其适用于低比特率下需要高保真度的应用。

排序理由该集群包含一篇详细介绍新型图像压缩模型的论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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Signal score: 14 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新型图像压缩模型的论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jiarun Chen, Kejun Wu, Li Li, Chengtao Cai, Zhengguo Li, Chia-Wen Lin · 2026-09-01 04:00

FLM：面向生成式图像压缩的频率感知语言模型

arXiv:2608.28687v1 Announce Type: new Abstract: Generative models have significantly improved the performance ceiling of image lossy compression at low bitrates by exploiting learned priors. However, the generated textures and semantic details may deviate from the source content,…

本文如何被排名

报道来源 [1]

FLM：面向生成式图像压缩的频率感知语言模型

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