As the use of neuromorphic, event-based vision sensors expands, the need for
compression of their output streams has increased. While their operational
principle ensures event streams are spatially sparse, the high temporal
resolution of the sensors can result in high data rates from the sensor
depending on scene dynamics. For systems operating in
communication-bandwidth-constrained and power-constrained environments, it is
essential to compress these streams before transmitting them to a remote
receiver. Therefore, we introduce a flow-based method for the real-time
asynchronous compression of event streams as they are generated. This method
leverages real-time optical flow estimates to predict future events without
needing to transmit them, therefore, drastically reducing the amount of data
transmitted. The flow-based compression introduced is evaluated using a variety
of methods including spatiotemporal distance between event streams. The
introduced method itself is shown to achieve an average compression ratio of
2.81 on a variety of event-camera datasets with the evaluation configuration
used. That compression is achieved with a median temporal error of 0.48 ms and
an average spatiotemporal event-stream distance of 3.07. When combined with
LZMA compression for non-real-time applications, our method can achieve
state-of-the-art average compression ratios ranging from 10.45 to 17.24.
Additionally, we demonstrate that the proposed prediction algorithm is capable
of performing real-time, low-latency event prediction.

该研究引入了一种基于流的方法，用于实时异步压缩事件流数据的生成，通过利用实时光流估计来预测未来事件，从而显著减少数据传输量，并展示了该方法在不同数据集上实现了平均 2.81 的压缩比，与 LZMA 压缩结合使用可以实现 10.45 到 17.24 的高压缩比。