@Article{Supelec693,
author = {Michel Barret and Jean-Louis Gutzwiller and Mohamed Hariti},
title = {{Low-Complexity Hyperspectral Image Coding Using Exogenous Orthogonal Optimal Spectral Transform (OrthOST) and Degree-2 Zerotrees}},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {2011},
volume = {49},
number = {5},
pages = {1557--1566},
month = {mai},
doi = {10.1109/TGRS.2010.2083671},
abstract = {We introduce a low-complexity codec for lossy compression of
hyperspectral images. These images have two kinds of
redundancies: 1) spatial; and 2) spectral. Our coder is based on
a compression scheme consisting in applying a 2-D discrete
wavelet transform (DWT) to each component and a linear transform
between components to reduce, respectively, spatial and spectral
redundancies. The DWT used is the Daubechies 9/7. However, the
spectral transform depends on the spectrometer sensor and the
kind of images to be encoded. It is calculated once and for all
on a set of images (the learning basis) from (only) one sensor,
thanks to Akam Bita et al. 's OrthOST algorithm that returns an
orthogonal spectral transform, whose optimality in high-rate
coding has been recently proved under mild conditions. The
spectral transform obtained in this way is applied to encode
other images from the same sensor. Quantization and entropy
coding are then achieved with a well-suited extension to
hyperspectral images of the Said and Pearlman's SPIHT algorithm.
Comparisons with a JPEG2000 codec using the Karhunen–Loève
transform (KLT) to reduce spectral redundancy show good
performance for our codec.
}
}