Spike Coding for Dynamic Vision Sensors

As an emerging kind of retinomorphic camera, the dynamic vision sensors (DVS) have shown great advantages in wide dynamic range and high temporal resolution in various applications such as autonomous driving and high-speed motion photography. However, how to compress the output spike data of DVS still remains a big challenge. To address this challenge, this paper firstly analyzes the spike firing mechanism and the redundancies of the spike data generated from DVS, and then introduces an efficient cube-based coding framework. Typically, a spike in DVS contains the location (the x-, y- addresses, the timestamp) and the polarity (On/Off). Three key strategies are designed to exploit the spatial and temporal characteristics of the spike location information for compression, including the adaptive macro-cube partitioning structure, the address-prior mode and the time-prior mode. Finally, the experimental results demonstrate that our approach achieves an impressive coding performance, with the average compression ratio of 19.519 over the original spike data, which is much higher than the results of conventional lossless coding algorithms.