  Recently, a novel bio-inspired spike camera has been proposed, which
continuously accumulates luminance intensity and fires spikes while the
dispatch threshold is reached. Compared to the conventional frame-based cameras
and the emerging dynamic vision sensors, the spike camera has shown great
advantages in capturing fast-moving scene in a frame-free manner with full
texture reconstruction capabilities. However, it is difficult to transmit or
store the large amount of spike data. To address this problem, we first
investigate the spatiotemporal distribution of inter-spike intervals and
propose an intensity-based measurement of spike train distance. Then, we design
an efficient spike coding method, which integrates the techniques of adaptive
temporal partitioning, intra-/inter-pixel prediction, quantization and entropy
coding into a unified lossy coding framework. Finally, we construct a PKU-Spike
dataset captured by the spike camera to evaluate the compression performance.
The experimental results on the dataset demonstrate that the proposed approach
is effective in compressing such spike data while maintaining the fidelity.
