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Indeed, each frame of aurora spectral images differs from the classical natural image and also from the frame of hyperspectral image. Existing lossless compression algorithms are not quite applicable. On the other hand, the key of compression is to decorrelate between pixels. We consider exploiting a DPCM-based scheme for the lossless compression because DPCM is effective for decorrelation. Such scheme makes use of two-dimensional redundancy both in the spatial and spectral domain with a relatively low complexity. Besides, we also parallel it for a faster computation speed. All codes are implemented on a structure consists of nested for loops of which the outer and the inner loops are respectively designed for spectral and spatial decorrelation. And the parallel version is represented on CPU platform using different numbers of cores.
Experimental results show that compared to traditional lossless compression methods, the DPCM scheme has great advantage in compression gain and meets the requirement of real-time transmission. Besides, the parallel version has expected computation performance with a high CPU utilization.
C-DPCM-RNS's prediction part consists of two-times trainings. The prediction coefficients obtained from the first training will be used in the linear predictor to compute all the predicted values and then the difference between original and predicted values in current band of current class. Only the non-noise spectra are used in the second training. The resulting prediction coefficients from the second training will be used for prediction and sent to the decoder. The two-times trainings remove part of the interference of noise spectra, and reaches a better compression effect than other methods based on regression prediction.
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