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arXiv · 2203.13686

Image Compression and Actionable Intelligence With Deep Neural Networks

Matthew Ciolino
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Abstract

If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connectivity device on the edge. This case requires that we use a different approach to deliver intelligence, particularly satellite imagery information, than normally employed. To address this, we propose a survey of information reduction techniques to deliver the information from a satellite image in a smaller package. We investigate four techniques to aid in the reduction of delivered information: traditional image compression, neural network image compression, object detection image cutout, and image to caption. Each of these mechanisms have their benefits and tradeoffs when considered for a disadvantaged user.

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Matthew Ciolino. 2022-03-22. Image Compression and Actionable Intelligence With Deep Neural Networks. https://arxiv.org/abs/2203.13686

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