Lossless Compression of Hyperspectral Imagery via Lookup Tables with Predictor Selection

摘要

We propose a new low-complexity algorithm for lossless compression of hyperspectral imagery using lookup tables along with a predictor selection mechanism. We first compute a locally averaged interband scaling (LAIS) factor for an estimate of the current pixel from the co-located one in the previous band. We then search via lookup tables in the previous band for the two nearest causal pixels that are identical to the pixel co-located to the current pixel. The pixels in the current band co-located to the causal pixels are used as two potential predictors. One of the two predictors that is closest to the LAIS estimate is chosen as the predictor for the current pixel. The method pushes lossless compression of the AVIRIS hyperspectral imagery to a new high with an average compression ratio of 3.47.