Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding

Abstract

These last years, research activities on multicomponent image compression have been expanded, due to the development of multispectral and hyperspectral image sensors which supply larger and larger amount of data. The end-users of such images become also more numerous and have various needs and various applications. The future earth observation systems, for instance, will use multi-, superand hyperspectral image sensors with higher resolutions leading to bigger amount of transmitted data. However the channel bandwidth for transmission is limited and therefore there is an interest of conceiving compression systems (onboard and on the ground) of multicomponent images which are not application dependent and which are compatible with the diversity of end-users’ needs. The components of a multicomponent image generally represent the same scene with different views depending on the wavelength. For data from different sensors, a preliminary step of image registration is therefore required as there is a high degree of dependence (or redundancies) between the various components: the usual spatial redundancy (between different pixels in each component) and the spectral redundancy (between the components). During the past two decades, different solutions have been proposed for multicomponent image coding. A solution currently adopted consists of using two different transformations, each with the goal of reducing only one of the two redundancies. In (Dragotti et al., 2000), a 2-D discrete wavelet transform (DWT) is used to reduce the spatial redundancies in each component while the Karhunen Loeve transform (KLT) is applied to reduce the spectral ones. In that paper, the quantization and entropy coding are achieved thanks to the well known SPIHT (Set Partitioning in Hierarchical Trees) codec by Said and Pearlman (Said & Pearlman, 1996) in its original version and in a modified version including VQ (vector quantization). In the same way, with the use of the 2-D DWT of (Antonini et al., 1992) (usually called the Daubechies 9/7), the authors of (Vaisey et al., 1998) use a lattice VQ with a stack run coder as quantization and entropy coding. More recently in (Rucker et al., 2005), the KLT associated with the Daubechies 9/7 2-D DWT and with EBCOT (Taubman, 2000; Taubman & Marcellin, 2002) for quantizing and entropy coding has been tested on Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding