A landscape–conditioned lossless wavelet–based predictive multispectral image compressor

We present a nonlinear lossless compressor designed for multispectral images consisting of few bands, and having greater spatial than spectral correlation. Our compressor is based on an 2D integer wavelet transform that reduces spatial correlation. Different models for statistical dependencies of wavelet detail coefficients are analyzed and tested to perform linear inter/intraband predictions. Band, class, scale and orientation are used as conditioning contexts to calculate predictions as well as to encode prediction errors with an adaptive arithmetic coder. A new mechanism is proposed for band-ordering, based on wavelet fine detail coefficients. Our compressor LLWP outperforms state of the art lossless compressors. It has random access capability, and can be applied to compress volumetric data having similar characteristics.