The large size of multispectral data files is currently a major issue in multispectral imaging. The transmission of multispectral data over networks, as well as the storage of large archives, are strongly limited, so that a clear need for good compression methods arises. In this paper, we explore the possibility of loss-less compression for multispectral data through a number of approximation methods that operate on the spectral domain. To evaluate the performance of these methods, we apply them to a representative spectra database, and consider the corresponding decrease In Information entropy as well as the classical file size ratio
Pellegri, P., Novati, G., Schettini, R. (2005). Multispectral loss-less compression using approximation methods. In 2005 International Conference on Image Processing (ICIP), Vols 1-5 (pp.638-641). IEEE [10.1109/ICIP.2005.1530136].
Multispectral loss-less compression using approximation methods
Schettini, R
2005
Abstract
The large size of multispectral data files is currently a major issue in multispectral imaging. The transmission of multispectral data over networks, as well as the storage of large archives, are strongly limited, so that a clear need for good compression methods arises. In this paper, we explore the possibility of loss-less compression for multispectral data through a number of approximation methods that operate on the spectral domain. To evaluate the performance of these methods, we apply them to a representative spectra database, and consider the corresponding decrease In Information entropy as well as the classical file size ratio
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