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3 June 2024 Research on optimization of CCSDS compression algorithm based on snapshot mosaic hyperspectral images
Yongfu Ping, Can Feng, Jiaju Tian, Zile Fang, Guosheng Lu, Haowen Huang, Zhuolin Han, Xudong Luo, Zhigang Zhao
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Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 131700Y (2024) https://doi.org/10.1117/12.3032191
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China
Abstract
Targeting the deficiencies of the full prediction mode and simplified prediction mode provided by the CCSDS 123.0-B-2 compression standard of the International Committee for Space Data Systems, this paper proposed a novel hybrid prediction mode combining the full prediction mode and the simplified prediction mode, and optimizes the local sum and local difference calculation parts of the predictor. The proposed hybrid prediction mode reduces the prediction complexity of edge pixels while ensuring the compression effect, which is favorable for deployment on FPGA hardware systems. The improved compression algorithm was tested and analyzed using a 25-channel hyperspectral image captured by a self-made snapshot mosaic hyperspectral imager developed by our research team. The experimental results show that the improved algorithm can realize the lossless compression of snapshot mosaic hyperspectral images (SSM HSI) with the compression effect of 2.67 ~ 4.32 bits/sample and the compression ratio of 1.86 ~ 3.01. The hybrid prediction mode can be applied in the fields of real-time imaging of unmanned aircraft-carried hyperspectral and wireless transmission of large-capacity hyperspectral images.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yongfu Ping, Can Feng, Jiaju Tian, Zile Fang, Guosheng Lu, Haowen Huang, Zhuolin Han, Xudong Luo, and Zhigang Zhao "Research on optimization of CCSDS compression algorithm based on snapshot mosaic hyperspectral images", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 131700Y (3 June 2024); https://doi.org/10.1117/12.3032191
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KEYWORDS
Image compression
Hyperspectral imaging
Mathematical optimization
Image quality standards
Data storage
Data transmission
Image restoration
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