@Article{Supelec417,
author = {Michel Narozny and Michel Barret and Dinh-Tuan Pham},
title = {{ICA based algorithms for computing optimal 1-D linear block transforms in variable high-rate source coding}},
journal = {Signal Processing},
year = {2008},
volume = {88},
number = {2},
pages = {268--283},
month = {février},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00278351/fr/},
doi = {10.1016/j.sigpro.2007.07.017},
abstract = {The Karhunen-Loève Transform (KLT) is optimal for transform
coding of Gaussian sources, however it is no more optimal, in
general, for non Gaussian sources. Furthermore, under the high
resolution quantization hypothesis, nearly everything is known
about the performance of a transform coding with entropy
constrained scalar quantization and mean square distortion. It is
then straightforward to find a criterion that, when minimized,
gives the optimal linear transform under the above mentioned
conditions. However, the optimal transform computation is
generally considered as a difficult task and the Gaussian
assumption is then used in order to simplify the calculus. In
this paper, we present the above mentioned criterion as a
contrast of Independent Component Analysis modified by an
additional term which is a penalty to nonorthogonality. Then
we adapt the icainf algorithm by Pham in order to compute the
transform minimizing the criterion either with no constraint or
with the orthogonality constraint. Finally, experimental results
show that the transforms we introduced can 1) outperform the KLT
on synthetic signals, 2) achieve slightly better PSNR for high-
rates and better visual quality (preservation of lines and
contours) for medium-to-low rates than the KLT and 2-D DCT on
grayscale natural images.}
}