发表机构
Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶多摄像头数据传输的带宽与能耗瓶颈，提出OASIS自适应视频压缩框架，结合轻量级传感器内压缩与任务感知的压缩比动态控制，在多种视觉任务上实现6倍数据压缩、5.8倍功耗降低和2.5倍延迟降低，性能损失不超过1.5%。
AI 中文摘要
计算机视觉系统是自动驾驶汽车中的关键构建模块，负责一系列感知任务。然而，它们通过来自多个摄像头的长距离通信链路产生大量数据传输，造成了关键的带宽和能量瓶颈。尽管诸如H.264之类的传统编解码器可以降低数据速率，但由于其高处理延迟和能耗，以及依赖于静态的用户定义压缩设置，它们不适合实时视觉系统。鉴于这些挑战，我们提出了OASIS，一个自适应视频压缩框架，它将轻量级传感器内压缩与任务感知的压缩比控制相结合。基于实时任务性能，它动态更新最优压缩比。实验结果表明，OASIS可推广到多种视觉任务，平均实现6倍数据压缩、5.8倍链路功耗降低和2.5倍链路延迟降低，同时性能下降最多不超过1.5%。
英文摘要
Computer vision systems are a key building block in an autonomous vehicle, responsible for a range of perception tasks. However, they incur massive data transmission over long communication links from multiple cameras, creating a critical bandwidth and energy bottleneck. Although conventional codecs such as H.264 can reduce data rates, they are ill-suited for real-time vision systems due to high processing latency and energy consumption, as well as their reliance on static user-defined compression settings. In light of these challenges, we propose OASIS, an adaptive video compression framework that integrates lightweight in-sensor compression with task-aware compression ratio control. Based on the real-time task performance, it dynamically updates the optimal compression ratio. Experimental results demonstrate that OASIS generalizes across multiple vision tasks, achieving on average a 6x data compression, 5.8x reduction in link power consumption, and 2.5x reduction in link latency, with at most 1.5% performance degradation.
