English(EN) Towards Practical 20 kbps Generative Video Compression via Adaptive Tokenization and Entropy Modeling

新的生成式视频压缩方法实现80%+感知节省

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一种新的生成式视频压缩方法，旨在提高超低比特率下的效率。该方法名为\method，将MiniMax-H3等模型的预训练视频表示适配为面向压缩的格式。它利用离散视频分词器和自回归熵模型来捕捉分词依赖性，并通过分组预测加快解码速度。在标准数据集上的实验表明，与现有方法相比，感知质量有了显著提高。 AI

影响这项研究可能带来更高效的视频流和存储解决方案，尤其是在带宽受限的环境中。

排序理由关于生成式视频压缩新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jiancheng Zhao, Jixiang Luo, Haibin Huang, Chi Zhang, Xuelong Li · 2026-11-06 05:00

面向实用化 20 kbps 生成式视频压缩：自适应分词与熵模型

arXiv:2611.08099v1 Announce Type: new Abstract: Video compression at ultra-low bitrates remains challenging due to the loss of perceptually important details under limited bit budgets. Recent generative video compression methods leverage pretrained video priors to improve percept…

本文如何被排名

报道来源 [1]

面向实用化 20 kbps 生成式视频压缩：自适应分词与熵模型

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