On optimal orthogonal transforms at high bit-rates using only second order statistics in multicomponent image coding with JPEG2000

Autor: Isidore Paul Akam Bita, Dinh-Tuan Pham, Michel Barret
Přispěvatelé: LUXSPACESarl, LUXSPACE Sarl, SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Laboratoire de Modélisation et Calcul (LMC - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2010
Předmět:
Discrete wavelet transform
Orthogonal transformation
Stationary wavelet transform
0211 other engineering and technologies
Image processing
Independent component analysis
02 engineering and technology
Multispectral image coding
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
JPEG2000
0202 electrical engineering
electronic engineering
information engineering
Electrical and Electronic Engineering
S transform
021101 geological & geomatics engineering
Mathematics
Karhunen–Loève theorem
Multicomponent image coding
Hyperspectral image coding
020206 networking & telecommunications
High rate transform coding
Control and Systems Engineering
Karhunen–Loève transform
Signal Processing
Computer Vision and Pattern Recognition
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Algorithm
Software
Data compression
Image compression
Zdroj: Signal Processing
Signal Processing, Elsevier, 2010, 90 (3), pp.753-758. ⟨10.1016/j.sigpro.2009.08.008⟩
ISSN: 0165-1684
1872-7557
Popis: International audience; 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–Loève 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.
Databáze: OpenAIRE
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