Lossless Compression of Data From Static and Mobile Dynamic Vision Sensors-Performance and Trade-Offs

• Main Authors: Nabeel Khan, Khurram Iqbal, Maria G. Martini
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Dynamic vision senor; neuromorphic computing; computer vision; spike coding; data compression; dictionary based compression
• Online Access: https://ieeexplore.ieee.org/document/9098874/

Dynamic Vision Sensors (DVS) are emerging retinomorphic visual capturing devices, with great advantages over conventional vision sensors in terms of wide dynamic range, low power consumption, and high temporal resolution. The bio-inspired approach of the DVS results in lower data rates than conventional vision sensors. Still, such data can be further compressed. Compression of DVS data is an emerging research area and a detailed performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed and latency. Moreover, the compression performance analysis is performed under diverse scenarios including stationary and mobile DVS. According to the detailed experimental analysis, Lempel-Ziv-Markov chain algorithm (LZMA) achieves the best compression ratios among all the considered strategies for the case when the DVS is static. On the other hand, Spike coding achieves the best compression ratios under the scenario when spike events are produced by a sensor in motion. However, both strategies result in low compression speed and high latency which restrict the applications of these strategies in real-time scenarios. The Brotli strategy achieves the best trade-off between compression ratio, speed and latency under static as well as mobile scenarios. We also observe a significant decrease in compression and decompression performance (in terms of ratio, speed and latency) of all the strategies under mobile DVS scenarios.