ROI-Packing: Efficient Region-Based Compression for Machine Vision
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915674523172864 |
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| author | Eimon, Md Eimran Hossain Krause, Alena Perera, Ashan Merlos, Juan Kalva, Hari Adzic, Velibor Furht, Borko |
| author_facet | Eimon, Md Eimran Hossain Krause, Alena Perera, Ashan Merlos, Juan Kalva, Hari Adzic, Velibor Furht, Borko |
| contents | This paper introduces ROI-Packing, an efficient image compression method tailored specifically for machine vision. By prioritizing regions of interest (ROI) critical to end-task accuracy and packing them efficiently while discarding less relevant data, ROI-Packing achieves significant compression efficiency without requiring retraining or fine-tuning of end-task models. Comprehensive evaluations across five datasets and two popular tasks-object detection and instance segmentation-demonstrate up to a 44.10% reduction in bitrate without compromising end-task accuracy, along with an 8.88 % improvement in accuracy at the same bitrate compared to the state-of-the-art Versatile Video Coding (VVC) codec standardized by the Moving Picture Experts Group (MPEG). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROI-Packing: Efficient Region-Based Compression for Machine Vision Eimon, Md Eimran Hossain Krause, Alena Perera, Ashan Merlos, Juan Kalva, Hari Adzic, Velibor Furht, Borko Computer Vision and Pattern Recognition This paper introduces ROI-Packing, an efficient image compression method tailored specifically for machine vision. By prioritizing regions of interest (ROI) critical to end-task accuracy and packing them efficiently while discarding less relevant data, ROI-Packing achieves significant compression efficiency without requiring retraining or fine-tuning of end-task models. Comprehensive evaluations across five datasets and two popular tasks-object detection and instance segmentation-demonstrate up to a 44.10% reduction in bitrate without compromising end-task accuracy, along with an 8.88 % improvement in accuracy at the same bitrate compared to the state-of-the-art Versatile Video Coding (VVC) codec standardized by the Moving Picture Experts Group (MPEG). |
| title | ROI-Packing: Efficient Region-Based Compression for Machine Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.09258 |