@InProceedings{Supelec541,
author = {Isidore-Paul Akambita and Michel Barret and Florio Dalla Vedova and Jean-Louis Gutzwiller},
title = {{Lossy compression of MERIS superspectral images with exogenous quasi optimal coding transforms}},
year = {2009},
booktitle = {{Proceedings of SPIE Satellite Data Compression, Communications, and Processing V}},
volume = {7455},
number = {0U},
pages = {9},
month = {août},
address = {San Diego (CA, USA)},
url = {http://dx.doi.org/10.1117/12.830488},
doi = {10.1117/12.830488},
abstract = {Our research focuses on reducing complexity of hyperspectral
image codecs based on transform and/or subband
coding, so they can be on-board a satellite. It is well-known
that the Karhunen-Loève Transform (KLT) can
be sub-optimal in transform coding for non Gaussian data.
However, it is generally recommended as the best
calculable linear coding transform in practice. Now, the
concept
and the computation of optimal coding transforms
(OCT), under low restrictive hypotheses at high bit-rates, were
carried out and adapted to a compression
scheme compatible with both the JPEG2000 Part2 standard and the
CCSDS recommendations for on-board
satellite image compression, leading to the concept and
computation of Optimal Spectral Transforms (OST).
These linear transforms are optimal for reducing spectral
redundancies of multi- or hyper-spectral images, when
the spatial redundancies are reduced with a fixed 2-D Discrete
Wavelet Transform (DWT). The problem of OST
is their heavy computational cost. In this paper we present the
performances in coding of a quasi optimal spectral
transform, called exogenous OrthOST, obtained by learning an
orthogonal OST on a sample of superspectral
images from the spectrometer MERIS. The performances are
presented in terms of bit-rate versus distortion for
four various distortions and compared to the ones of the KLT.
We
observe good performances of the exogenous
OrthOST, as it was the case on Hyperion hyper-spectral images
in
previous works.}
}