Abstract
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.
Highlights
Dynamic Vision Sensors (DVS) [1], [2] mimic the visual processing characteristics of living organisms, i.e., they capture only changes in scene reflectance
We investigate the performance of DVS data specific compression approach
We introduce the datasets considered for the tests and the simulation set-up in Section III, while comparative simulation results are reported in Sections IV and V, where the trade-offs to be addressed are discussed
Summary
Dynamic Vision Sensors (DVS) [1], [2] mimic the visual processing characteristics of living organisms, i.e., they capture only changes in scene reflectance. N. Khan et al.: Lossless Compression of Data From Static and Mobile DVS-Performance and Trade-Offs rendered DVS frames, spike events fired owing to the increase in luminance intensity (polarity p = 1) are represented by the ‘‘green’’ color. Because the output of the DVS is quite different from conventional frame-based image sequences, existing computer vision techniques cannot be directly applied to the series of neuromorphic spike events To address this issue, many algorithms have been tailored to leverage spike events data for a diverse range of applications (object detection, classifications, optical flow estimation, etc.). The output of the DVS is a multivariate stream of integers (X and Y spatial addresses, time-stamp field, and the polarity flag) It is worth investigating the performance on DVS data of general purpose lossless.
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