Flow-Based Visual Stream Compression for Event Cameras

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 (CR) of 2.70 on a variety of event-camera data sets with the evaluation configuration used. That compression is achieved with a median temporal error of 0.31 ms and an average spatiotemporal event-stream distance of 4.72. When combined with Lempel-Ziv–Markov chain algorithm compression for non-real-time applications, our method can achieve state-of-the-art average CRs ranging from 9.29 to 13.01. Additionally, we demonstrate that the proposed prediction algorithm is capable of performing real time, low-latency event prediction.