Scalable Event-Based Video Streaming for Machines with MoQ
This work tackles the crucial issue of scalable and efficient streaming for event-based video data, which has been largely ignored in prior research focused on application development.
The paper addresses the problem of data transmission for neuromorphic event-based video sensors, which are used in computer vision applications, by proposing a new low-latency event streaming format based on the Media Over QUIC protocol draft.
Lossy compression and rate-adaptive streaming are a mainstay in traditional video steams. However, a new class of neuromorphic ``event'' sensors records video with asynchronous pixel samples rather than image frames. These sensors are designed for computer vision applications, rather than human video consumption. Until now, researchers have focused their efforts primarily on application development, ignoring the crucial problem of data transmission. We survey the landscape of event-based video systems, discuss the technical issues with our recent scalable event streaming work, and propose a new low-latency event streaming format based on the latest additions to the Media Over QUIC protocol draft.