Fast Two-Stage Lempel-Ziv Lossless Numeric Telemetry Data Compression Using a Neural Network Predictor

dc.creatorLogeswaran,Rajasvaran
dc.date2004
dc.date.accessioned2024-02-06T12:53:11Z
dc.date.available2024-02-06T12:53:11Z
dc.descriptionLempel-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.
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-010-09-1199
dc.identifierhttps://lib.jucs.org/article/28292/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/8619
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 10(9): 1199-1211
dc.subjectLempel-Ziv
dc.subjectneural networks
dc.subjectprediction
dc.subjectlossless compression
dc.subjecttwo-stage
dc.titleFast Two-Stage Lempel-Ziv Lossless Numeric Telemetry Data Compression Using a Neural Network Predictor
dc.typeResearch Article
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