Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables

Abstract

We propose an enhancement to the algorithm for lossless compression of hyperspectral images using lookup tables (LUTs). The original LUT method searched the previous band for a pixel of equal value to the pixel colocalized with the one to be predicted. The pixel in the same position as the obtained pixel in the current band is used as a predictor. LUTs were used to speed up the search. The LUT method has also been extended into a method called Locally Averaged Interband Scaling (LAIS)-LUT that uses two LUTs per band. One of the two LUT predictors that is the closest one to the LAIS estimate is chosen as the predictor for the current pixel. We propose the uniform quantization of the colocated pixels before using them for indexing the LUTs. The use of quantization reduces the size of the LUTs by an order of magnitude. The results show that the proposed method outperforms previous methods; a 3% increase in compression efficiency was observed compared to the current state-of-the-art method, LAIS-LUT.