Low-Power Lossless Image Compression on Small Satellite Edge using Spiking Neural Network

The emerging trend of small satellites for earth observation missions has enabled commercial organisations to exploit the horizon for various business applications related to weather forecasting/monitoring, Land Use Land Cover (LULC) classifications, disaster (such as oil spill or forest fire) monitoring etc. However, the limited power and computational capacity of these small satellites arising out of the size and weight restrictions have posed newer challenges, primarily related to low-power on-board data processing and transmission. One possible approach is to harness the capabilities of the evolving neuromorphic computing paradigm for such low-power computing requirements. One possible application can be lossless compression of high-resolution earth observation images before sending those downstream to ground stations for further analysis. In this paper, we propose a novel method of lossless image compression based on classical Arithmetic Encoding that exploits the low power computing capability of Spiking Neural Networks and neuromorphic platforms. We experimentally prove that our SNN approach achieves compression ratio at-par with state of the art ANN methods with an estimated 2.5× power efficiency and 50% lower latency with a much smaller model - thereby enabling on-board image compression and at the same time, saving on a corresponding amount of energy during transmission.