A novel method for lossless compression of arbitrarily shaped regions of interest in hyperspectral imagery
Hongda Shen, W. David Pan, Yi Wang · 2015
We propose a novel algorithm for lossless compression of regions of interest (ROI) in hyperspectral images. The algorithm can compress arbitrarily shaped ROIs as specified by a binary map. The algorithm separates the boundary pixels from the full-context pixels within the ROI and applies Golomb-Rice encoders with different parameters on the boundary and full-context ROI pixels respectively. Experimental results show that the proposed algorithm provides larger compression than JPL's low-complexity hyperspectral image compressing method when applied on individual ROI's.