# SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera

arXiv: https://arxiv.org/abs/2306.14108 · HTML: https://mlsanity.com/paper/2306.14108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [Learning a No-Reference Quality Metric for Single-Image Super-Resolution](https://mlsanity.com/paper/1612.05890/) (2016)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [A Retina-inspired Sampling Method for Visual Texture Reconstruction](https://mlsanity.com/paper/1907.08769/) (2019)
- [End-to-end Flow Correlation Tracking with Spatial-temporal Attention](https://mlsanity.com/paper/1711.01124/) (2017)
- [Spike Camera and Its Coding Methods](https://mlsanity.com/paper/2104.04669/) (2021)
- [An Efficient Coding Method for Spike Camera using Inter-Spike Intervals](https://mlsanity.com/paper/1912.09669/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Joint Visual Compression and Perception Framework for Neuralmorphic Spiking Camera](https://mlsanity.com/paper/2503.02725/) (2025)
- [SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities](https://mlsanity.com/paper/2507.16151/) (2025)

## Abstract (arXiv)

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we introduce an effective and robust representation of spike streams. Based on this representation, we propose a novel learned spike compression framework using scene recovery, variational auto-encoder plus spike simulator. To our knowledge, it is the first data-trained model for efficient and robust spike stream compression. Extensive experimental results show that our method outperforms the conventional and learning-based codecs, contributing a strong baseline for learned spike data compression.