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Bi-Level Video Codec for Machine Vision Embedded Applications
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-6484-9260
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2013 (English)In: Elektronika Ir Elektrotechnika, ISSN 1392-1215, Vol. 19, no 8, p. 93-96Article in journal (Refereed) Published
Abstract [en]

Wireless Visual Sensor Networks (WVSN) are feasible today due to the advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) cameras, low power electronics, distributed computing and radio transceivers. The energy budget in WVSN is limited due to the small form factor of the Visual Sensor Nodes (VSNs) and the wireless nature of the application. The images captured by VSN contain huge amount of data which leads to high communication energy consumptions. Hence there is a need for designing efficient algorithms which are computationally less complex and provide high compression ratio. The change coding and Region of Interest (ROIs) coding are the options for data reduction of the VSN. But, for higher number of objects in the images, the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Bi-Level Video Codec (BVC) for several representative machine vision applications. We proposed to implement image coding, change coding and ROI coding at the VSN and to select the smallest bit stream among the three. Results show that the compression performance of the BVC for such applications is always better than that of change coding and ROI coding.

Place, publisher, year, edition, pages
2013. Vol. 19, no 8, p. 93-96
Keywords [en]
Wireless sensor networks, low power electronics, embedded computing, image communication
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-20650DOI: 10.5755/j01.eee.19.8.5401ISI: 000325684100020Scopus ID: 2-s2.0-84885620407Local ID: STCOAI: oai:DiVA.org:miun-20650DiVA, id: diva2:678301
Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2016-10-20Bibliographically approved

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Khursheed, KhursheedImran, MuhammadAhmad, NaeemO'Nils, Mattias

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