Simultaneous Denoising and Compression for DVS with Partitioned Cache-Like Spatiotemporal Filter
Qinghang Zhao, Yixi Ji, Jiaqi Wang, Jinjian Wu, Guangming Shi · 2025
Dynamic vision sensor (DVS) is a novel neuromorphic imaging device that asynchronously generates event data corresponding to changes in light intensity at each pixel. However, the differential imaging paradigm of DVS renders it highly sensitive to background noise. Additionally, the substantial volume of event data produced in a very short time presents significant challenges for data transmission and processing. In this work, we present a novel spatiotemporal filter design, named PCLF, to achieve simultaneous denoising and compression for the first time. The PCLF employs a hierarchical memory structure that utilizes symmetric multi-bank cache-like row and column memories to store event data from a partitioned pixel array, which exhibits low memory complexity of O(m + n) for an$\mathrm{m}\times \mathrm{n}$DVS. Furthermore, we propose a probability-based criterion to effectively control the compression ratio. We have implemented our design on an FPGA, demonstrating capabilities for real-time operation$(\leq 60\ \text{ns})$and low power consumption$(< 200\text{mW})$. Extensive experiments conducted on real-world DVS data across various tasks indicate that our design enables a reduction of event data by 30% to 68%, while maintaining or even enhancing the performance of the tasks.