  Event cameras have the ability to capture asynchronous per-pixel brightness
changes, called "events", offering advantages over traditional frame-based
cameras for computer vision applications. Efficiently coding event data is
critical for transmission and storage, given the significant volume of events.
This paper proposes a novel double deep learning-based architecture for both
event data coding and classification, using a point cloud-based representation
for events. In this context, the conversions from events to point clouds and
back to events are key steps in the proposed solution, and therefore its impact
is evaluated in terms of compression and classification performance.
Experimental results show that it is possible to achieve a classification
performance of compressed events which is similar to one of the original
events, even after applying a lossy point cloud codec, notably the recent
learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate
reduction. Experimental results also demonstrate that events coded using JPEG
PCC achieve better classification performance than those coded using the
conventional lossy MPEG Geometry-based Point Cloud Coding standard.
Furthermore, the adoption of learning-based coding offers high potential for
performing computer vision tasks in the compressed domain, which allows
skipping the decoding stage while mitigating the impact of coding artifacts.
