Optimal compression of vibration data with lifting wavelet transform and context-based arithmetic coding

Yang Zhang, Paul Hutchinson, Nicholas A.J. Lieven, Jose Nunez-Yanez

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
220 Downloads (Pure)

Abstract

This paper proposes an adaptive vibration signal compression scheme composed of a lifting discrete wavelet transform (LDWT) with set-partitioning embedded blocks (SPECK) that efficiently sorts the wavelet coefficients by significance. The output of the SPECK module is input to an optimized context-based arithmetic coder that generates the compressed bitstream. The algorithm is deployed as part of a reliable and effective health monitoring technology for machines and civil constructions (e.g. power generation system). This application area relies on the collection of large quantities of high quality vibration sensor data that needs to be compressed before storing and transmission. Experimental results indicate that the proposed method outperforms state-of-the-art coders, while retaining the characteristics in the compressed vibration signals to ensure accurate event analysis. For the same quality level, up to 59.41% bitrate reduction is achieved by the proposed method compared to JPEG2000.
Original languageEnglish
Title of host publication2017 25th European Signal Processing Conference (EUSIPCO 2017)
Subtitle of host publicationProceedings of a meeting held 28 August - 2 September 2017, Kos, Greece
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1996-2000
Number of pages5
ISBN (Electronic)9780992862671
ISBN (Print)9781538607510
DOIs
Publication statusPublished - Jan 2018
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Publication series

Name
ISSN (Print)2076-1465

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period28/08/172/09/17

Fingerprint Dive into the research topics of 'Optimal compression of vibration data with lifting wavelet transform and context-based arithmetic coding'. Together they form a unique fingerprint.

Cite this