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Proceedings Paper

Hyperspectral image compression using low complexity integer KLT and three-dimensional asymmetric significance tree
Author(s): Jing Huang; Rihong Zhu
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Paper Abstract

A lossy to lossless three-dimensional (3D) compression of hyperspectral images is presented. On the spectral dimension, a low complexity reversible integer Karhunen-Loève 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.

Paper Details

Date Published: 3 September 2009
PDF: 10 pages
Proc. SPIE 7444, Mathematics for Signal and Information Processing, 74440I (3 September 2009); doi: 10.1117/12.826815
Show Author Affiliations
Jing Huang, Nanjing Univ. of Posts and Telecommunications (China)
Rihong Zhu, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7444:
Mathematics for Signal and Information Processing
Franklin T. Luk; Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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