Fast piecewise linear predictors for lossless compression of hyperspectral imagery

The work presented here deals with the design of predictors for the lossless compression of hyperspectral imagery. The large number of spectral bands that characterize hyperspectral imagery give it properties that can be exploited when performing compression. Specifically, in addition to the spatial correlation which is similar to all images, the large number of spectral bands means a high spectral correlation also. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. This work deals with the design of predictors for the decorrelation stage which are both fast and good. Fast implies low complexity, which was achieved by having predictors with no multiplications, only comparisons and additions. Good means predictors that have performance close to the state of the art. To achieve this, both spectral and spatial correlations are used for the predictor. The performance of the developed predictors are compared to those in the most widely known algorithms, LOCO-I, used in JPEG-Lossless, and CALIC-Extended, the original version of which had the best compression performance of all the algorithms submitted to the JPEG-LS committee. The developed algorithms are shown to be much less complex than CALIC-Extended with better compression performance.