发表机构
Instituto Superior Técnico, University of Lisbon; Instituto de Telecomunicações; Instituto Universitário de Lisboa (ISCTE-IUL)（里斯本大学高级技术学院; 电信研究所; 里斯本大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出两种事件压缩流水线，在任务驱动框架下评估五种新指标，可可靠预测压缩导致的任务性能下降，为事件数据编码优化提供直接指导。
AI 中文摘要
事件相机生成具有微秒级时间分辨率的异步稀疏数据流，但在中等到高速运动场景中，每秒可产生多达数亿个事件，带来显著的带宽和存储挑战。因此有损压缩对于实际部署至关重要，然而现有的事件流失真指标无法可靠预测压缩导致的任务级性能下降，迫使编解码器优化依赖昂贵的任务特定评估。为解决这一差距，本文提出两种根本不同的事件压缩流水线：i）基于聚合的流水线，将事件流转换为基于极性的直方图帧，以使用传统图像编解码器JPEG 2000进行压缩；ii）无帧的基于点云的流水线，使用基于八叉树的编解码器G-PCC将事件直接编码为三维点。随后在统一的任务驱动评估框架中对两种流水线进行评估，该框架关联事件流失真与下游应用性能，涉及四个代表性任务：i）视频重建、ii）目标检测、iii）光流估计，以及延迟敏感型任务iv）参考相对协议下的异步特征跟踪。据作者所知，本文首次将五种基于分类的失真指标应用于事件压缩，并与现有事件流指标进行基准测试。实验结果表明，所提出的指标可在不同编码框架下可靠预测压缩导致的任务性能下降，证明事件流失真评估可替代重复的任务特定评估，为未来事件数据编码方案的开发和优化提供直接指导。
英文摘要
Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce as many as hundreds of millions of events per second, creating significant bandwidth and storage challenges. Lossy compression is therefore essential for practical deployment, yet existing event stream distortion metrics fail to reliably predict compression-induced degradation at the task level, forcing codec optimization to rely on expensive task-specific evaluations. To address this gap, this paper introduces two fundamentally different event compression pipelines: i) an aggregation-based pipeline that converts the event stream into polarity-based histogram frames for compression with the conventional image codec JPEG 2000, and ii) a frame-free point cloud-based pipeline that codes events natively as 3D points using the octree-based codec G-PCC. Both pipelines are then assessed within a unified task-driven evaluation framework that relates event stream distortion to downstream application performance across four representative tasks: i) video reconstruction, ii) object detection, iii) optical flow estimation, and a delay-sensitive task iv) asynchronous feature tracking under a reference-relative protocol. Building on this framework, five classification-based distortion metrics are applied to event compression for the first time, to the best of the authors' knowledge, and benchmarked against existing event stream metrics. Experimental results demonstrate that the proposed metrics reliably predict compression-induced task degradation across different coding frameworks. This demonstrates that event stream distortion assessment can be an efficient alternative to repeated task-specific evaluation, providing direct guidance for the development and optimization of future event data coding solutions.
