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Image Compression and Actionable Intelligence With Deep Neural Networks

arXiv 2203.13686 ↗First posted 22 Mar 2022cs.LG cs.CV eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 22 Mar 2022.

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10 of 30 references.

References · 10

  1. 2017Mask R-CNNcited by 7,259
  2. 2018YOLOv3: An Incremental Improvementcited by 2,652
  3. 2017Exploring Models and Data for Remote Sensing Image Caption Generationcited by 195
  4. 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
  5. 2018xView: Objects in Context in Overhead Imagerycited by 145
  6. 2016Learning a No-Reference Quality Metric for Single-Image Super-Resolutioncited by 143
  7. 2018Deep Convolutional AutoEncoder-based Lossy Image Compressioncited by 42
  8. 2018Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compressioncited by 6
  9. 2020Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Picturescited by 5
  10. 2019Perceptual Quality Study on Deep Learning based Image Compressioncited by 4

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