An Asynchronous Intensity Representation for Framed and Event Video Sources
This work addresses the need for a source-agnostic video representation to enable real-time intensity-based applications for event cameras, representing an incremental improvement in video processing.
The paper tackles the challenge of representing video data from both framed and event cameras by introducing an asynchronous intensity representation, which reduces sample rates by over half with only a 4.5 drop in VMAF quality score and achieves lower latency with 2000x higher temporal resolution than state-of-the-art methods.
Neuromorphic "event" cameras, designed to mimic the human vision system with asynchronous sensing, unlock a new realm of high-speed and high dynamic range applications. However, researchers often either revert to a framed representation of event data for applications, or build bespoke applications for a particular camera's event data type. To usher in the next era of video systems, accommodate new event camera designs, and explore the benefits to asynchronous video in classical applications, we argue that there is a need for an asynchronous, source-agnostic video representation. In this paper, we introduce a novel, asynchronous intensity representation for both framed and non-framed data sources. We show that our representation can increase intensity precision and greatly reduce the number of samples per pixel compared to grid-based representations. With framed sources, we demonstrate that by permitting a small amount of loss through the temporal averaging of similar pixel values, we can reduce our representational sample rate by more than half, while incurring a drop in VMAF quality score of only 4.5. We also demonstrate lower latency than the state-of-the-art method for fusing and transcoding framed and event camera data to an intensity representation, while maintaining $2000\times$ the temporal resolution. We argue that our method provides the computational efficiency and temporal granularity necessary to build real-time intensity-based applications for event cameras.