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

arXiv: https://arxiv.org/abs/1912.09669 · HTML: https://mlsanity.com/paper/1912.09669/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer](https://mlsanity.com/paper/2208.12527/) (2022)
- [SpikeCV: Open a Continuous Computer Vision Era](https://mlsanity.com/paper/2303.11684/) (2023)
- [SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream](https://mlsanity.com/paper/2403.11222/) (2024)
- [Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views](https://mlsanity.com/paper/2307.00821/) (2023)
- [SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors](https://mlsanity.com/paper/2407.03771/) (2024)
- [Finding Visual Saliency in Continuous Spike Stream](https://mlsanity.com/paper/2403.06233/) (2024)
- [Uncertainty Guided Depth Fusion for Spike Camera](https://mlsanity.com/paper/2208.12653/) (2022)
- [SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera](https://mlsanity.com/paper/2306.14108/) (2023)
- [SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream](https://mlsanity.com/paper/2409.15176/) (2024)
- [SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams](https://mlsanity.com/paper/2503.20315/) (2025)
- [Spike Stream Denoising via Spike Camera Simulation](https://mlsanity.com/paper/2304.03129/) (2023)
- [Seeing the Unseen in Low-light Spike Streams](https://mlsanity.com/paper/2509.23304/) (2025)

## Abstract (arXiv)

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.