TransVFC: A Transformable Video Feature Compression Framework for Machines
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912653265338368 |
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| author | Sun, Yuxiao Zhao, Yao Liu, Meiqin Yao, Chao Bai, Huihui Lin, Chunyu Lin, Weisi |
| author_facet | Sun, Yuxiao Zhao, Yao Liu, Meiqin Yao, Chao Bai, Huihui Lin, Chunyu Lin, Weisi |
| contents | Nowadays, more and more video transmissions primarily aim at downstream machine vision tasks rather than humans. While widely deployed Human Visual System (HVS) oriented video coding standards like H.265/HEVC and H.264/AVC are efficient, they are not the optimal approaches for Video Coding for Machines (VCM) scenarios, leading to unnecessary bitrate expenditure. The academic and technical exploration within the VCM domain has led to the development of several strategies, and yet, conspicuous limitations remain in their adaptability for multi-task scenarios. To address the challenge, we propose a Transformable Video Feature Compression (TransVFC) framework. It offers a compress-then-transfer solution and includes a video feature codec and Feature Space Transform (FST) modules. In particular, the temporal redundancy of video features is squeezed by the codec through the scheme-based inter-prediction module. Then, the codec implements perception-guided conditional coding to minimize spatial redundancy and help the reconstructed features align with downstream machine perception.After that, the reconstructed features are transferred to new feature spaces for diverse downstream tasks by FST modules. To accommodate a new downstream task, it only requires training one lightweight FST module, avoiding retraining and redeploying the upstream codec and downstream task networks. Experiments show that TransVFC achieves high rate-task performance for diverse tasks of different granularities. We expect our work can provide valuable insights for video feature compression in multi-task scenarios. The codes are at https://github.com/Ws-Syx/TransVFC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TransVFC: A Transformable Video Feature Compression Framework for Machines Sun, Yuxiao Zhao, Yao Liu, Meiqin Yao, Chao Bai, Huihui Lin, Chunyu Lin, Weisi Image and Video Processing Nowadays, more and more video transmissions primarily aim at downstream machine vision tasks rather than humans. While widely deployed Human Visual System (HVS) oriented video coding standards like H.265/HEVC and H.264/AVC are efficient, they are not the optimal approaches for Video Coding for Machines (VCM) scenarios, leading to unnecessary bitrate expenditure. The academic and technical exploration within the VCM domain has led to the development of several strategies, and yet, conspicuous limitations remain in their adaptability for multi-task scenarios. To address the challenge, we propose a Transformable Video Feature Compression (TransVFC) framework. It offers a compress-then-transfer solution and includes a video feature codec and Feature Space Transform (FST) modules. In particular, the temporal redundancy of video features is squeezed by the codec through the scheme-based inter-prediction module. Then, the codec implements perception-guided conditional coding to minimize spatial redundancy and help the reconstructed features align with downstream machine perception.After that, the reconstructed features are transferred to new feature spaces for diverse downstream tasks by FST modules. To accommodate a new downstream task, it only requires training one lightweight FST module, avoiding retraining and redeploying the upstream codec and downstream task networks. Experiments show that TransVFC achieves high rate-task performance for diverse tasks of different granularities. We expect our work can provide valuable insights for video feature compression in multi-task scenarios. The codes are at https://github.com/Ws-Syx/TransVFC. |
| title | TransVFC: A Transformable Video Feature Compression Framework for Machines |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2503.23772 |