  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.
