Packet Compressed Sensing Imaging (PCSI): Robust Image Transmission over Noisy Channels
September 24, 2020 Β· Entered Twilight Β· π arXiv.org
"Last commit was 5.0 years ago (β₯5 year threshold)"
Evidence collected by the PWNC Scanner
Repo contents: .gitignore, CHANGELOG.md, HAB2sstv.bmp, LICENSE, README.md, appveyor.yml, dist.sh, docs, pcsi, pcsiGUI-linuxOD.spec, pcsiGUI-linuxOF.spec, pcsiGUI.py, pcsiSerial.py, pcsiSimulator.py
Authors
Scott Howard, Grant Barthelmes, Cara Ravasio, Lisa Huang, Benjamin Poag, Varun Mannam
arXiv ID
2009.11455
Category
eess.IV: Image & Video Processing
Cross-listed
cs.MM
Citations
2
Venue
arXiv.org
Repository
https://github.com/maqifrnswa/PCSI
β 39
Last Checked
2 months ago
Abstract
Packet Compressed Sensing Imaging (PCSI) is digital unconnected image transmission method resilient to packet loss. The goal is to develop a robust image transmission method that is computationally trivial to transmit (e.g., compatible with low-power 8-bit microcontrollers) and well suited for weak signal environments where packets are likely to be lost. In other image transmission techniques, noise and packet loss leads to parts of the image being distorted or missing. In PCSI, every packet contains random pixel information from the entire image, and each additional packet received (in any order) simply enhances image quality. Satisfactory SSTV resolution (320x240 pixel) images can be received in ~1-2 minutes when transmitted at 1200 baud AFSK, which is on par with analog SSTV transmission time. Image transmission and reception can occur simultaneously on a computer, and multiple images can be received from multiple stations simultaneously - allowing for the creation of "image nets." This paper presents a simple computer application for Windows, Mac, and Linux that implements PCSI transmission and reception on any KISS compatible hardware or software modem on any band and digital mode.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Image & Video Processing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Kvasir-SEG: A Segmented Polyp Dataset
R.I.P.
π»
Ghosted
Deep Learning for Hyperspectral Image Classification: An Overview
R.I.P.
π»
Ghosted
U-Net and its variants for medical image segmentation: theory and applications
R.I.P.
π»
Ghosted
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
R.I.P.
π»
Ghosted