Image Compression and Actionable Intelligence With Deep Neural Networks
This addresses the challenge of providing actionable intelligence to edge devices with poor connectivity, though it is an incremental survey rather than a novel solution.
The paper tackles the problem of delivering satellite imagery intelligence to disadvantaged users with low connectivity by surveying four information reduction techniques: traditional image compression, neural network image compression, object detection image cutout, and image-to-caption, analyzing their benefits and tradeoffs.
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.