English(EN) Tree-Structured Vector Quantization For Efficient And Progressive Image Compression

Tree-VQ 提供具有分层结构的渐进式图像压缩

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

研究人员推出了一种新颖的渐进式图像压缩框架 Tree-VQ，该框架利用分层二叉树结构。这种方法允许对单个压缩表示进行渐进式细化，从而可以从比特流的前缀中解码重建。该框架包含一个与前缀兼容的熵模型和感知率优化调度，以优化性能和效率。实验表明，Tree-VQ 在感知压缩质量、参数效率和延迟方面均优于现有方法。 AI

影响这项新的压缩技术可能导致更高效的视觉数据存储和传输，并可能影响严重依赖图像处理的 AI 应用。

排序理由该集群包含一篇详细介绍新技术的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Xinkun Wang, Tianyi Xu, Qingyu Luo, Mingming Ma, Changzhe Jiao, Fu Li, Yi Niu · 2026-09-04 04:00

用于高效渐进式图像压缩的树状向量量化

arXiv:2609.03641v1 Announce Type: new Abstract: Vector-quantization based image compression has achieved strong rate--distortion performance, yet most of them still produce a separate compressed representation for each target bitrate. Such variable-rate behavior allows one model …

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用于高效渐进式图像压缩的树状向量量化

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