Efficient Video Diffusion with Sparse Information Transmission for Video Compression

Efficient Video Diffusion with Sparse Information Transmission for Video Compression | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/efficient-video-diffusion-with-sparse-information-transmission-for-video-compression

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-20
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

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Canonical ID efficient-video-diffusion-with-sparse-information-transmission-for-video-compression | Route /signal-canvas/efficient-video-diffusion-with-sparse-information-transmission-for-video-compression

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Efficient Video Diffusion with Sparse Information Transmission for Video Compression

PDF: https://arxiv.org/pdf/2603.18501v1

Repository: https://github.com/MingdeZhou/Diff-SIT

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-20T21:29:18.280Z

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Subject: Efficient Video Diffusion with Sparse Information Transmission for Video Compression

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Evidencepartial
Extensive experiments on multiple datasets demonstrate that Diff-SIT establishes a new state-of-the-art in perceptual quality and temporal consistency, particularly in the challenging ultra-low-bitrate regime.
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However, at ultra-low bitrates, traditional end-to-end compression models tend to produce blurry images of poor perceptual quality.
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Besides, existing generative compression methods often treat video frames independently and show limitations in time coherence and efficiency.
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The STEM sparsely encodes the original frame sequence into an information-rich intermediate sequence, achieving significant bitrate savings.
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Subsequently, the ODFTE processes this intermediate sequence as a whole, which exploits the temporal correlation.
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During this process, our proposed Frame Type Embedder (FTE) guides the diffusion model to perform adaptive reconstruction according to different frame types to optimize the overall quality.
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Beyond standard distortion metrics, perceptual quality and temporal consistency are also critical.
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Efficient Video Diffusion with Sparse Information Transmission for Video Compression

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