Low-Bitrate Video Compression through Semantic-Conditioned Diffusion

• URL: http://arxiv.org/abs/2512.00408v1
• Date: Sat, 29 Nov 2025 09:38:16 GMT
• Title: Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
• Authors: Lingdong Wang, Guan-Ming Su, Divya Kothandaraman, Tsung-Wei Huang, Mohammad Hajiesmaili, Ramesh K. Sitaraman,
• Abstract summary: We propose a severe failure that transmits only the most meaningful information while relying on generative detail for priors for priors.<n>A conditional video reconstructs high-quality, temporally coherent videos from semantic, appearance, and motion cues.
• Score: 19.21409064179896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel accuracy and human perception. We propose a semantic video compression framework named DiSCo that transmits only the most meaningful information while relying on generative priors for detail synthesis. The source video is decomposed into three compact modalities: a textual description, a spatiotemporally degraded video, and optional sketches or poses that respectively capture semantic, appearance, and motion cues. A conditional video diffusion model then reconstructs high-quality, temporally coherent videos from these compact representations. Temporal forward filling, token interleaving, and modality-specific codecs are proposed to improve multimodal generation and modality compactness. Experiments show that our method outperforms baseline semantic and traditional codecs by 2-10X on perceptual metrics at low bitrates.

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