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Detecting and Coding Region of Interests in Bi-Level Images for Data Reduction in Wireless Visual Sensor Network
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0002-6484-9260
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier. (STC)ORCID-id: 0000-0003-1923-3843
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier. (STC)
2012 (engelsk)Inngår i: Wireless and Mobile Computing, Networking and Communications (WiMob), 2012 IEEE 8th International Conference on, IEEE conference proceedings, 2012, s. 705-712Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. The VSNs acquire images of the area of interest in the field, perform some local processing on these images and transmit the results using an embedded wireless transceiver. The energy consumption on transmitting the results wirelessly is correlated with the information amount that is being transmitted.  The images acquired by the VSNs contain huge amount of data due to many kinds of redundancies in the images. Suitable bi-level image compression standards can efficiently reduce the information amount in images and will thus be effective in reducing the communication energy consumption in the WVSN. But compression capability of the bi-level image compression standards is limited to the underline compression algorithm. Further data reduction can be achieved by detecting Region of Interest (ROI) in the bi-level images and then coding these ROIs using bi-level image compression method. We explored the compression performance of the lossless ROI detection and coding method for various kinds of changes such as different shapes, locations and number of objects in the continuous set of frames. The CCITT Group 4, JBIG2 and Gzip are used for coding the detected ROIs. We concluded that CCITT Group 4 is a better choice for coding the ROIs in the Bi-level images because of its comparatively good compression performance and less computational complexity. This paper is intended to be a resource for the researchers interested in reducing the amount of data in the bi-level images for energy constrained WVSNs.

sted, utgiver, år, opplag, sider
IEEE conference proceedings, 2012. s. 705-712
Emneord [en]
ROI coding, Image Coding, Wireless Visual Sensor Network, Energy Consumption.
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-18030DOI: 10.1109/WiMOB.2012.6379153Scopus ID: 2-s2.0-84872055183Lokal ID: STCISBN: 978-1-4673-1429-9 (tryckt)OAI: oai:DiVA.org:miun-18030DiVA, id: diva2:579368
Konferanse
The 1st International Workshop on Wireless Multimedia Sensor Networks (WMSN 2012) in conjunction with The 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2012)
Tilgjengelig fra: 2012-12-20 Laget: 2012-12-20 Sist oppdatert: 2016-10-20bibliografisk kontrollert
Inngår i avhandling
1. Investigation of intelligence partitioning and data reduction in wireless visual sensor network
Åpne denne publikasjonen i ny fane eller vindu >>Investigation of intelligence partitioning and data reduction in wireless visual sensor network
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
sted, utgiver, år, opplag, sider
Sundsvall: Mid Sweden University, 2013. s. 208
Serie
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 150
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-20976 (URN)STC (Lokal ID)978-91-87103-75-9 (ISBN)STC (Arkivnummer)STC (OAI)
Veileder
Tilgjengelig fra: 2014-01-08 Laget: 2014-01-08 Sist oppdatert: 2016-10-20bibliografisk kontrollert

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