Paper Info
Title
Improved back end for integer PCA and wavelet transforms for lossless compression of multispectral images
Abstract
Remote sensing produces large amounts of digital data that is collected into databases. Since a variety of applications utilize multispectral data, the data cannot be compressed with lossy methods for some user communities. In this paper, we propose improvements for the combination of two reversible methods for the lossless compression of multispectral images. Our improvements are three-fold: number of bits allocated to the coefficients from PCA is not constant but it is based on heuristics, difference between consecutive coefficients are entropy-coded, also the back-end is modified so that all bands are separately entropy coded, i.e. instead of one entropy coder we used several. Depending on the AVIRIS image, the actual compression ratios, calculated from the files sizes, were in the range from 3.05 to 3.21.
Year
DOI
Venue
2002
10.1109/ICPR.2002.1048287
Pattern Recognition, 2002. Proceedings. 16th International Conference  
Keywords
Field
DocType
data compression,eigenvalues and eigenfunctions,image coding,principal component analysis,remote sensing,wavelet transforms,AVIRIS image,databases,entropy coder,integer principal component analysis,lossless compression,multispectral images compression,remote sensing,wavelet transforms
Computer vision,Pattern recognition,Lossy compression,Computer science,Multispectral image,Compression ratio,Multispectral pattern recognition,Artificial intelligence,Data compression,Image compression,Lossless compression,Wavelet transform
Conference
Volume
ISSN
Citations 
2
1051-4651
5
PageRank 
References 
Authors
0.73
2
2
Name
Order
Citations
PageRank
Jarno Mielikäinen1153.39
Arto Kaarna217427.50