  With several advantages over conventional RGB cameras, event cameras have
provided new opportunities for tackling visual tasks under challenging
scenarios with fast motion, high dynamic range, and/or power constraint. Yet
unlike image/video compression, the performance of event compression algorithm
is far from satisfying and practical. The main challenge for compressing events
is the unique event data form, i.e., a stream of asynchronously fired event
tuples each encoding the 2D spatial location, timestamp, and polarity (denoting
an increase or decrease in brightness). Since events only encode temporal
variations, they lack spatial structure which is crucial for compression. To
address this problem, we propose a novel event compression algorithm based on a
quad tree (QT) segmentation map derived from the adjacent intensity images. The
QT informs 2D spatial priority within the 3D space-time volume. In the event
encoding step, events are first aggregated over time to form polarity-based
event histograms. The histograms are then variably sampled via Poisson Disk
Sampling prioritized by the QT based segmentation map. Next, differential
encoding and run length encoding are employed for encoding the spatial and
polarity information of the sampled events, respectively, followed by Huffman
encoding to produce the final encoded events. Our Poisson Disk Sampling based
Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based
optimal allocation. On average, our algorithm achieves greater than 6x
compression compared to the state of the art.
