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English(EN) Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion

扩散模型通过主动采样实现超低比特率视频压缩

研究人员开发了ActDiff-VC,一种用于超低比特率场景视频压缩的新型基于扩散的框架。该方法仅在必要时策略性地传输关键帧,并使用跟踪点轨迹来总结时间信息。然后,条件扩散解码器合成缺失的帧,从而在显著的速率限制下实现感知上逼真的重建。实验表明,与现有编解码器相比,比特率显著降低,感知质量得到提高。 AI

影响 引入了一种新的基于扩散的高效视频压缩方法,可能影响流媒体和带宽受限的应用。

排序理由 这是一篇详细介绍视频压缩新方法的学术论文。

在 arXiv cs.CV 阅读 →

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扩散模型通过主动采样实现超低比特率视频压缩

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Amirhosein Javadi, Shirin Saeedi Bidokhti, Tara Javidi ·

    基于条件控制扩散的超低比特率视频压缩主动采样

    arXiv:2605.02849v1 Announce Type: new Abstract: Diffusion models provide a powerful generative prior for perceptual reconstruction at ultra-low bitrates, but effective video compression requires controlling the generative process using highly compact conditioning signals. In this…

  2. arXiv cs.CV TIER_1 English(EN) · Tara Javidi ·

    通过条件控制扩散实现超低比特率视频压缩的主动采样

    Diffusion models provide a powerful generative prior for perceptual reconstruction at ultra-low bitrates, but effective video compression requires controlling the generative process using highly compact conditioning signals. In this work, we present ActDiff-VC, a diffusion-based …