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Title: Application specific compression : final report.

Abstract

With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% ofmore » the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3.« less

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
950654
Report Number(s):
SAND2008-6566
TRN: US200911%%9
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DATA TRANSMISSION; EFFICIENCY; OPTIMIZATION; Sensors-Data processing.; Data compression (Telecommunication); Data compression (Computer science)

Citation Formats

Melgaard, David Kennett, Byrne, Raymond Harry, Myers, Daniel S., Harrison, Carol D., Lee, David S., Lewis, Phillip J., and Carlson, Jeffrey J. Application specific compression : final report.. United States: N. p., 2008. Web. doi:10.2172/950654.
Melgaard, David Kennett, Byrne, Raymond Harry, Myers, Daniel S., Harrison, Carol D., Lee, David S., Lewis, Phillip J., & Carlson, Jeffrey J. Application specific compression : final report.. United States. doi:10.2172/950654.
Melgaard, David Kennett, Byrne, Raymond Harry, Myers, Daniel S., Harrison, Carol D., Lee, David S., Lewis, Phillip J., and Carlson, Jeffrey J. Mon . "Application specific compression : final report.". United States. doi:10.2172/950654. https://www.osti.gov/servlets/purl/950654.
@article{osti_950654,
title = {Application specific compression : final report.},
author = {Melgaard, David Kennett and Byrne, Raymond Harry and Myers, Daniel S. and Harrison, Carol D. and Lee, David S. and Lewis, Phillip J. and Carlson, Jeffrey J.},
abstractNote = {With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% of the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3.},
doi = {10.2172/950654},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Dec 01 00:00:00 EST 2008},
month = {Mon Dec 01 00:00:00 EST 2008}
}

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