Multispectral image compression algorithms

This paper presents a data compression algorithm capable of significantly reducing the amounts of information contained in multispectral and hyperspectral images. The loss of information ranges from a perceptually lossless level, achieved at 20-30:1 compression ratios, to a one where exploitation of the images is still possible (over 100:1 ratios). A one-dimensional transform coder removes the spectral redundancy, and a two-dimensional wavelet transform removes the spatial redundancy of multispectral images. The transformed images are subsequently divided into active regions that contain significant wavelet coefficients. Each active block is then hierarchically encoded using multidimensional bitmap trees. Application of reversible histogram equalization methods on the spectral bands can significantly increase the compression/distortion performance. Landsat Thematic Mapper data are used to illustrate the performance of the proposed algorithm