Experimental Demonstration of Video Compression Sensing Free Space Optical Transmission System Based on Spatial Coding

Wireless video image transmission is now widely used in scientific research and daily life, and laser beams have received more and more attention as an excellent information carrier. We combined the compressed sensing image algorithm with space laser communication technology and developed a free-spa...

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Vydáno v:2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP) s. 1427 - 1431
Hlavní autoři: Li, Jinwang, Dong, Keyan, Liu, Tianci, Song, Yansong, Chen, Ci
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 19.04.2024
Témata:
Atmospheric propagation channel
Compressed sensing
Free space optical video transmission system
Free-space optical communication
Image coding
Image communication
Image reconstruction
Indexes
Optical imaging
Optical receivers
Optical sensors
Signal processing algorithms
Spatial coding
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Shrnutí:Wireless video image transmission is now widely used in scientific research and daily life, and laser beams have received more and more attention as an excellent information carrier. We combined the compressed sensing image algorithm with space laser communication technology and developed a free-space optical image transmission system utilizing the compressed sensing algorithm. However, due to the influence of atmospheric channels, the communication bit error rate increases, and there are errors in compressed image transmission, which seriously affects the quality of the deconstructed image at the receiving end. In response to this phenomenon, firstly, we built a space optical video transmission board based on FPGA and conducted experiments to quantitatively analyze the relationship between environmental impact and receiver image quality. Then, we propose a spatial coding method that divides the compressed image into different subframes for encoding according to position information. After collecting the data at the receiving end, the position is first allocated according to the encoding and the compressed image is reorganized. This solution solves the problem of individual data loss leading to overall line misalignment of the image. Finally, we conducted comparative experiments using the algorithm proposed in this paper and the traditional compressed sensing algorithm. Experimental results show that the method proposed in this paper improves the PSNR index by more than 3dB and the SSIM index by more than 15% under different algorithm sampling rates, which has a significant optimization effect on the reconstruction quality of atmospheric channel optical transmission images.
DOI:10.1109/ICSP62122.2024.10743677