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Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0002-6484-9260
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)
2012 (English)In: SPIE: Proc. SPIE 8437, 84370M (2012), 2012Conference paper, Poster (Refereed)
Abstract [en]

Wireless Visual Sensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at Visual Sensor Nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN.

Place, publisher, year, edition, pages
2012.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-18029DOI: 10.1117/12.923716ISI: 000305693900019Scopus ID: 2-s2.0-84861956053Local ID: STCISBN: 978-0-8194-9129-9 (print)OAI: oai:DiVA.org:miun-18029DiVA: diva2:579365
Conference
SPIE, Real-Time Image and Video Processing
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2016-10-20Bibliographically approved

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Bi-Level Image Compression Methods(373 kB)577 downloads
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Khursheed, KhursheedImran, MuhammadAhmad, NaeemO'Nils, Mattias
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