Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor

Lempel-Ziv (LZ) is a popular lossless data compression algorithm that produces good compression performance, but suffers from relatively slow processing speed. This paper proposes an enhanced version of the Lempel-Ziv algorithm, through incorporation of a neural pre-processor in the popular predicto...

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Main Author: Logeswaran,, R
Format: Article
Published: 2004
Subjects:
Online Access:http://shdl.mmu.edu.my/2523/
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author Logeswaran,, R
author_facet Logeswaran,, R
author_sort Logeswaran,, R
building MMU Institutional Repository
collection Online Access
description Lempel-Ziv (LZ) is a popular lossless data compression algorithm that produces good compression performance, but suffers from relatively slow processing speed. This paper proposes an enhanced version of the Lempel-Ziv algorithm, through incorporation of a neural pre-processor in the popular predictor-encoder implementation. It is found that in addition to the known dramatic performance increase in compression ratio that multi-stage predictive techniques achieve, the results in this paper show that overall processing speed for the multi-stage scheme can increase by more than 15 times for lossless LZ compression of numeric telemetry data. The benefits of the proposed scheme may be expanded to other areas and applications.
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spelling mmu-25232011-08-22T06:32:08Z http://shdl.mmu.edu.my/2523/ Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor Logeswaran,, R QA75.5-76.95 Electronic computers. Computer science Lempel-Ziv (LZ) is a popular lossless data compression algorithm that produces good compression performance, but suffers from relatively slow processing speed. This paper proposes an enhanced version of the Lempel-Ziv algorithm, through incorporation of a neural pre-processor in the popular predictor-encoder implementation. It is found that in addition to the known dramatic performance increase in compression ratio that multi-stage predictive techniques achieve, the results in this paper show that overall processing speed for the multi-stage scheme can increase by more than 15 times for lossless LZ compression of numeric telemetry data. The benefits of the proposed scheme may be expanded to other areas and applications. 2004 Article NonPeerReviewed Logeswaran,, R (2004) Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor. JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 10 (9). pp. 1199-1211. ISSN 0948-695X
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Logeswaran,, R
Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title_full Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title_fullStr Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title_full_unstemmed Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title_short Fast two-stage Lempel-Ziv lossless numeric telemetry data compression using a neural network predictor
title_sort fast two-stage lempel-ziv lossless numeric telemetry data compression using a neural network predictor
topic QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/2523/