Quantifying information content in data compression using the autocorrelation function

S. Sanborn; X. Ma

doi:10.1109/LSP.2004.842264

Quantifying information content in data compression using the autocorrelation function

S. Sanborn^*, X. Ma

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

This letter presents an algorithm that uses time-series statistical techniques to analyze the information content changes of an individual signal time series subjected to data compression. Autocorrelation-based methods are used to analyze the residuals between the original and processed signals. T-statistic analysis of Autocorrelation Function (ACF) coefficients is used to determine an Upper Specification Limit (USL) of the data compression ratio. The approach provides a continuous data quality measurement that is useful in datalogging systems design and online performance monitoring of such systems. The proposed algorithms can be widely applied to remote monitoring and diagnosis systems. A practical example is also described in the letter.

Original language	English
Pages (from-to)	230-233
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	12
Issue number	3
DOIs	https://doi.org/10.1109/LSP.2004.842264
Publication status	Published - 1 Mar 2005

Keywords

Autocorrelation function
Compression efficiency
Data compression
Data quality metric
Information content analysis
Monitoring and diagnostics

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10.1109/LSP.2004.842264

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title = "Quantifying information content in data compression using the autocorrelation function",

abstract = "This letter presents an algorithm that uses time-series statistical techniques to analyze the information content changes of an individual signal time series subjected to data compression. Autocorrelation-based methods are used to analyze the residuals between the original and processed signals. T-statistic analysis of Autocorrelation Function (ACF) coefficients is used to determine an Upper Specification Limit (USL) of the data compression ratio. The approach provides a continuous data quality measurement that is useful in datalogging systems design and online performance monitoring of such systems. The proposed algorithms can be widely applied to remote monitoring and diagnosis systems. A practical example is also described in the letter.",

keywords = "Autocorrelation function, Compression efficiency, Data compression, Data quality metric, Information content analysis, Monitoring and diagnostics",

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N2 - This letter presents an algorithm that uses time-series statistical techniques to analyze the information content changes of an individual signal time series subjected to data compression. Autocorrelation-based methods are used to analyze the residuals between the original and processed signals. T-statistic analysis of Autocorrelation Function (ACF) coefficients is used to determine an Upper Specification Limit (USL) of the data compression ratio. The approach provides a continuous data quality measurement that is useful in datalogging systems design and online performance monitoring of such systems. The proposed algorithms can be widely applied to remote monitoring and diagnosis systems. A practical example is also described in the letter.

AB - This letter presents an algorithm that uses time-series statistical techniques to analyze the information content changes of an individual signal time series subjected to data compression. Autocorrelation-based methods are used to analyze the residuals between the original and processed signals. T-statistic analysis of Autocorrelation Function (ACF) coefficients is used to determine an Upper Specification Limit (USL) of the data compression ratio. The approach provides a continuous data quality measurement that is useful in datalogging systems design and online performance monitoring of such systems. The proposed algorithms can be widely applied to remote monitoring and diagnosis systems. A practical example is also described in the letter.

KW - Autocorrelation function

KW - Compression efficiency

KW - Data compression

KW - Data quality metric

KW - Information content analysis

KW - Monitoring and diagnostics

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JO - IEEE Signal Processing Letters

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ER -

Quantifying information content in data compression using the autocorrelation function

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Keywords

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