用扩散模型实现超低码率视频压缩，只传关键帧和轨迹点。
Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
- 按内容动态选关键帧，用稀疏轨迹编码时间变化。
- 在相同画质下比现有方法节省64.6%码率，重建质量提升显著。
- 适合超低码率场景，尤其对带宽敏感的视频应用
扩散模型为超低码率下的感知重建提供了强大的生成先验，但高效视频压缩需要利用高度紧凑的条件信号来控制生成过程。本文提出ActDiff-VC，一种面向超低码率场景的基于扩散模型的视频压缩框架。该方法将视频划分为可变长度片段，仅在必要时传输关键帧，并通过一组追踪的点轨迹紧凑地总结时序动态。以这些稀疏信号为条件，条件扩散解码器合成缺失帧，实现在严苛码率约束下的感知真实重建。为此，我们引入两种机制：内容自适应关键帧选择与预算感知的稀疏轨迹选择，共同实现紧凑而有效的生成重建条件。在UVG和MCL-JCV基准上的实验表明，ActDiff-VC在保持相同NIQE指标下实现最高64.6%的码率降低；在相当码率下，KID提升达64.6%，FID改善37.7%；相比学习型及扩散基基线，在超低码率下展现出更优的感知率失真权衡。
原文摘要 · Abstract (English)
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 video compression framework for the ultra-low-bitrate regime. Our method partitions videos into variable-length segments, transmits keyframes only when needed, and summarizes temporal dynamics using a compact set of tracked point trajectories. Conditioned on these sparse signals, a conditional diffusion decoder synthesizes the remaining frames, enabling perceptually realistic reconstruction under severe rate constraints. To support this design, we introduce two mechanisms: content-adaptive keyframe selection and budget-aware sparse trajectory selection, which together enable compact yet effective conditioning for generative reconstruction. Experiments on the UVG and MCL-JCV benchmarks show that ActDiff-VC achieves up to 64.6\% bitrate reduction at matched NIQE, improves KID by up to 64.6\% and FID by up to 37.7\% at comparable bitrates against strong learned codecs, and delivers favorable perceptual rate--distortion trade-offs relative to learned and diffusion-based baselines in the ultra-low-bitrate regime.
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