An Efficient Coding Method for Spike Camera using Inter-Spike Intervals

TL;DR

This paper introduces a novel spike coding method for spike camera data that effectively compresses large spike datasets by leveraging inter-spike intervals and predictive coding, maintaining data fidelity.

Contribution

It proposes an innovative intensity-based spike coding framework combining adaptive partitioning, prediction, and entropy coding for efficient spike data compression.

Findings

Significant reduction in spike data size while preserving image quality

Effective compression demonstrated on the PKU-Spike dataset

Improved data transmission and storage efficiency for spike cameras

Abstract

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.