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English(EN) Generative Video Compression Based on Hierarchical Referencing

新的GVCHR方法通过分层引用增强生成式视频压缩

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的生成式视频压缩方法GVCHR，该方法以分层方式组织潜在帧。该方法通过为经常被用作引用的低层帧分配更多比特，从而提高了编码效率并减少了伪影传播。GVCHR采用了分层时间上下文挖掘技术以实现有效的潜在编码，以及分层注意力适配器以在生成式重建过程中将注意力限制在相关的引用上。实验表明，GVCHR在BD率方面显著优于最先进的方法，并且在视觉质量方面也有显著提高。 AI

影响 这项研究可能带来更高效的视频压缩技术，从而提高生成式视频应用的质量并降低带宽要求。

排序理由 该集群描述了一篇详细介绍一种新的生成式视频压缩方法的论文。

在 Hugging Face Daily Papers 阅读 →

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新的GVCHR方法通过分层引用增强生成式视频压缩

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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    基于分层引用的生成式视频压缩

    Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be encoded efficiently while serving as denoising conditions. However, existing methods neither carefully design reference and quali…

  2. arXiv cs.CV TIER_1 English(EN) · Daowen Li, Ding Ding, Zifu Zhang, Kai Li, Ying Chen ·

    基于分层引用的生成式视频压缩

    arXiv:2608.11618v1 Announce Type: new Abstract: Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be encoded efficiently while serving as denoising conditions. However, existing meth…

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