Hyperspectral image compression using low complexity integer KLT and three-dimensional asymmetric significance tree

Abstract

A lossy to lossless three-dimensional (3D) compression of hyperspectral images is presented. On the spectral dimension, a low complexity reversible integer Karhunen-Loeve transform (KLT) is used to fully exploit the spectral redundancy, while two-dimensional spatial combinative lifting algorithm (SCLA)-based integer wavelet transform is applied on the spatial dimension. At the low complexity KLT, the calculation processing of covariance matrix is carried out on a subset of vectors that is pseudorandomly selected from the complete set of spectral vectors. The transform matrix is factorized into triangular elementary reversible matrices (TERM) for reversible integer mapping and the lifting scheme is applied to implement integer KLT. The 3D asymmetric significance tree structure is then constructed from the 3D asymmetric orientation tree in 3D transformed domain. Each coefficient is then encoded by the significance test of the 3D asymmetric significance tree node at each bitplane instead of ordered lists to track the significance status of the tree or block sets and coefficients. This algorithm has low complexity and can be applied to lossy to lossless progressive transmission.