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).
Abstract (translated)
本文介绍了ROI-Packing，这是一种专门为机器视觉设计的高效图像压缩方法。通过优先处理对最终任务准确性至关重要的感兴趣区域（ROI），并有效打包这些区域同时丢弃不太相关的信息，ROI-Packing能够在不重新训练或微调最终任务模型的情况下实现显著的压缩效率。在五个数据集和两个流行的任务——对象检测和实例分割上的全面评估表明，在不牺牲最终任务准确性的前提下，ROI-Packing能够将比特率降低高达44.10%，并且与由运动图像专家组（MPEG）标准化的最新视频编码技术Versatile Video Coding (VVC) 编码器相比，在相同的比特率下还能提高8.88%的准确性。
URL
https://arxiv.org/abs/2512.09258
