SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera
This work addresses the storage and transmission burden for spike camera users, offering a novel solution for a specific imaging domain.
The authors tackled the problem of compressing data from spike cameras, which produce large volumes due to high temporal resolution, by proposing SpikeCodec, an end-to-end learned compression framework. Their method outperformed conventional and learning-based codecs, establishing a strong baseline for spike data compression.
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.