聚焦关键区域，压缩图像同时提升机器视觉精度。
ROI-Packing: Efficient Region-Based Compression for Machine Vision
- 按目标重要性分块压缩，保留关键区域数据。
- 比特率降低44.10%且任务准确率不变，或同比特率下精度提升8.88%。
- 无需重训练模型，适合部署于检测与分割任务。
本文提出ROI-Packing，一种专为机器视觉设计的高效图像压缩方法。该方法优先保留对下游任务准确性至关重要的感兴趣区域（ROI），并高效打包这些区域，同时丢弃不相关数据，从而在不需重新训练或微调下游模型的前提下，实现显著的压缩效率提升。在五个数据集和两个主流任务（目标检测与实例分割）上的全面评估表明，与由动态影像专家组（MPEG）标准化的先进通用视频编码（VVC）相比，该方法可实现最高达44.10%的比特率降低，且不损失任务准确率；或在相同比特率下，精度提升8.88%。
原文摘要 · Abstract (English)
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).
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