@Article{Supelec560,
author = {Isidore-Paul Akambita and Michel Barret and Dinh-Tuan Pham},
title = {{On optimal orthogonal transforms at high bit-rates using only second order statistics in multicomponent image coding with JPEG2000}},
journal = {Signal Processing},
year = {2010},
volume = {90},
number = {3},
pages = {753-758},
month = {mars},
url = {http://dx.doi.org/10.1016/j.sigpro.2009.08.008},
doi = {10.1016/j.sigpro.2009.08.008},
abstract = {We study a JPEG2000 compatible multicomponent image compression scheme, which consists in applying a discrete wavelet transform (DWT) to each component of the image and a spectral linear transform between components. We consider the case of a spectral transform which adapts to the image and a 2-D DWT with fixed coefficients. In Akam Bita et al. (accepted for publication, [6]) we gave a criterion minimized by optimal spectral transforms. Here, we derive a simplified criterion by treating the transformed coefficients in each subband as having a Gaussian distribution of variance depending on the subband. Its minimization under orthogonality constraint is shown to lead to a joint approximate diagonalization problem, for which a fast algorithm (JADO) is available. Performances in coding of the transform returned by JADO are compared on hyper- and multi-spectral images with the Karhunen–Loeve transform (KLT) and the optimal transform (without Gaussianity assumption) returned by the algorithm OrthOST introduced in Akam Bita et al. (accepted for publication, [6]). For hyper- (resp. multi-) spectral images, we observe that JADO returns a transform which performs appreciably better than (resp. as well as) the KLT at medium to high bit-rates, nearly attaining (resp. slightly below) the performances of the transform returned by OrthOST, with a significantly lower complexity than the algorithm OrthOST.}
}