Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913006416297984 |
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| author | Wang, Lingdong Su, Guan-Ming Kothandaraman, Divya Huang, Tsung-Wei Hajiesmaili, Mohammad Sitaraman, Ramesh K. |
| author_facet | Wang, Lingdong Su, Guan-Ming Kothandaraman, Divya Huang, Tsung-Wei Hajiesmaili, Mohammad Sitaraman, Ramesh K. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_00408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Bitrate Video Compression through Semantic-Conditioned Diffusion Wang, Lingdong Su, Guan-Ming Kothandaraman, Divya Huang, Tsung-Wei Hajiesmaili, Mohammad Sitaraman, Ramesh K. Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Low-Bitrate Video Compression through Semantic-Conditioned Diffusion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.00408 |