THE LOSSLESS COMPRESSION OF AVIRIS IMAGES BY VECTOR QUANTIZATION

The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by vector quantization. Th is paper outlines the results of an investigation of lossless vector q uantization of 224-band Airborne/Visible Infrared Imaging Spectrometer (AVIRIS) images, Various vector formation techniques are identified a nd suitable quantization parameters are investigated, A new technique, mean-normalized vector quantization (MNVQ), is proposed which produce s compression performances approaching the theoretical minimum compres sed image entropy of 5 bits/pixel, Images are compressed from original image entropies of between 8.28 and 10.89 bits/pixel to between 4.83 and 5.90 bits/pixel.