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A hyper/multispectral imaging system splits the light spectrum into more than three frequency bands (dozens to hundreds) and records each of the images separately as a set of monochrome images. This type of technique increases the number of acquisition channels in the visible spectrum and extends channel acquisition to the light that is outside the sensitivity of the human eye. Such systems offer several advantages over conventional RGB imaging and have, therefore, attracted increasing interest in the past few years. However, multispectral uncompressed images, in which a single image-band may occupy hundreds of megabytes, often require high capacity storage. Compression is thus necessary to facilitate both the storage and the transmission of multispectral images.
Generally, a multispectral image is represented as a 3D cube with one spectral and two spatial dimensions. The fact that a multispectral image consists of a series of narrow and contiguously spectral bands of the same scene produces a highly correlated sequence of images. This particularity differentiates multispectral images from volumetric ones with three isotropic spatial dimensions, and also from videos with one temporal and two spatial dimensions. Conventional compression methods are not optimal for multispectral image compression, which is why compression algorithms need to be adapted to this type of image.
One of the most efficient compression methods for monochrome images compression is the JPEG 2000 (Boliek, Christopoulos, & Majani, 2000; Boliek, Majani, Houchin, Kasner, & Carlander, 2000; Christopoulos, Skodras, & Ebrahimi, 2000; Taubman, 2000; Taubman, Marcellin, & Rabbani, 2002). Its extension to multi/hyperspectral images yields to different approaches. These approaches depend on the manner of which one consider the multi/hyperspectral cube after the decorrelation stage (Figure 1):
Figure 1. Graphical representation of the three compression approaches
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In the Multi-2D approach, each image band of the multi/hyperspectral image is considered separately (2D wavelets + 2D SPIHT) (Du & Fowler, 2007; Kaarna, Toivanen, & Keränen, 2006; Mielikäinen & Kaarna, 2002),
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The whole cube is considered as input leading to two main implementations: the Hybrid approach (3D wavelets + 2D SPIHT), as used in (Penna, Tillo, Magli, & Olmo, 2006), and the Full 3D approach (3D wavelets + 3D SPIHT). For this latter we used an anisotropic 3D wavelets decomposition. Many works in literature have explored the 3D wavelet transform for multi/hyperspectral image compression but they only use isotropic 3D wavelets (same type of wavelets following all directions) (Kaarna, Zemcik, Kaelviainen, & Parkkinen, 1998; Kaarna & Parkkinen, 1998, 2000a, 2000b; Kaarna, 2001; Kaarna et al., 2006; Kim, Xiong, & Pearlman, 2000; Lim, Sohn, & Lee, 2001; Mielikäinen & Kaarna, 2002; Penna et al., 2006; Tang, Cho, & Pearlman, 2003), but according to our best knowledge no have us a compression method based on an anisotropic wavelets decomposition.
We tested the three compression approaches with the same lifting scheme wavelet transform and compared them. To provide a more objective benchmark, we propose a framework of evaluation composed of seven metrics in addition to the classic PSNR. These metrics evaluate the quality of reconstruction in terms of signal, spectral reflectance and perceptive aspects.