Lossless Compression for Hyperspectral Images Using Back Pixel Search with Adaptive Threshold

Abstract

The special statistical property produced by radiative correction has a serious impact on prediction accuracy. Back pixel search (BPS) algorithm is currently the most effective way to solve this problem. However, the effectiveness of BPS algorithm depends on optimal threshold and the prediction accuracy of the first prediction. In this paper, an effective lossless compression method for hyperspectral image based on conventional recursive least squares (CRLS) algorithm and BPS algorithm with adaptive threshold is proposed. Firstly, the CRLS predictor is adopted in the first prediction to improve the accuracy of predicted reference values. Afterwards, a recursive error mean estimation with scaling factor is used to estimate the optimal search threshold in the BPS predictor. Finally, the arithmetic encoder is used to entropy-encode the residuals generated by prediction. The experimental results on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images set show that this method significantly improves the compression effect and reduces the computational complexity compared with the typical methods already reported.