A Lossless Compression Algorithm with Hardware Implementation for Dynamic Vision Sensor

Nowadays, with the increase resolution of Dynamic Vision Sensor (DVS), efficient compression algorithm for event stream is needed urgently. Conventional DVS system encodes event data in address event representation (AER) for output while ignores the data redundancy imposed by the correlation of events. To address this challenge, this paper first analyzes the spatiotemporal characteristics of event stream and the impact of readout circuits. Based on the analysis, the context-based encoding strategies for spatial address, timestamp and polarity of events are proposed respectively with the consideration of data flow in DVS hardware. Besides, the hardware architecture with high parallelism is presented to implement the compression algorithm, which achieves high throughput at an affordable cost. The hardware is implemented in the 55nm process as part of a 512x512 resolution DVS. The experimental results demonstrate that our methods achieves higher average compression ratio compared to conventional and DVS-specific coding algorithms.