Spatial compression algorithm for the analysis of very large multivariate images
Abstract
A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.
- Inventors:
-
- Albuquerque, NM
- Issue Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 942284
- Patent Number(s):
- 7400772
- Application Number:
- 10/772,805
- Assignee:
- Sandia Corporation (Albuquerque, NM)
- Patent Classifications (CPCs):
-
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04N - PICTORIAL COMMUNICATION, e.g. TELEVISION
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Keenan, Michael R. Spatial compression algorithm for the analysis of very large multivariate images. United States: N. p., 2008.
Web.
Keenan, Michael R. Spatial compression algorithm for the analysis of very large multivariate images. United States.
Keenan, Michael R. Tue .
"Spatial compression algorithm for the analysis of very large multivariate images". United States. https://www.osti.gov/servlets/purl/942284.
@article{osti_942284,
title = {Spatial compression algorithm for the analysis of very large multivariate images},
author = {Keenan, Michael R},
abstractNote = {A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jul 15 00:00:00 EDT 2008},
month = {Tue Jul 15 00:00:00 EDT 2008}
}
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