Lossless compression of hyperspectral imasges using improved Locally Averaged Interband Scaling Lookup Tables

In this paper, an improved algorithm for the Locally Averaged Interband Scaling Lookup Tables (LAIS-LUT) prediction method is proposed, which is based on prediction using lookup tables. The predictor of LAIS-LUT is selected from one of two lookup tables regardless of whether or not it is close to the LAIS-LUT prediction. However, there are cases when the predictions are not accurate. The algorithm provides more choices for the predictor than the LAIS-LUT method, according to size of the values returned by lookup tables and the LAIS estimate. Therefore, the proposed method can give a more accurate prediction than LAIS-LUT method. Experimental results show that the proposed method outperforms both LUT and LAIS-LUT methods. In addition, the proposed algorithm is comparable to the Multiband Lookup Tables (M-LUT) method.