{"ID":2830514,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09258","arxiv_id":"2512.09258","title":"ROI-Packing: Efficient Region-Based Compression for Machine Vision","abstract":"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).","short_abstract":"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 req...","url_abs":"https://arxiv.org/abs/2512.09258","url_pdf":"https://arxiv.org/pdf/2512.09258v1","authors":"[\"Md Eimran Hossain Eimon\",\"Alena Krause\",\"Ashan Perera\",\"Juan Merlos\",\"Hari Kalva\",\"Velibor Adzic\",\"Borko Furht\"]","published":"2025-12-10T02:29:53Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}