Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method

• Published in: Information Sciences
• Main Authors: W. Kong, J. Wu, Z. Hu, G. Jeon, M. Anisetti, E. Damiani
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2017
• Subjects: Aurora spectral image; Bi-dimensional decorrelation; Lossless compression; Online; Parallel; Control and Systems Engineering; Theoretical Computer Science; Software; Computer Science Application; Computer Vision and Pattern Recognition; Information Systems and Management; Artificial Intelligence; Settore INF/01 - Informatica; Antarc*; Antarctic
• Online Access: http://hdl.handle.net/2434/470640; https://doi.org/10.1016/j.ins.2016.11.008

In this paper, we propose a lossless compression method to resolve the limitations in the real-time transmission of aurora spectral images. This method bi-dimensionally decorrelates the spatial and spectral domains and effectively removes side information of recursively computed coefficients to achieve high quality rapid compression. Experiments on data sets captured from the Antarctic Zhongshan Station show that the proposed algorithm can meet real-time requirements by using parallel processing to achieve outstanding compression ratio performance with low computational complexity.