A new image size reduction model for an efficient visual sensor network

Kaljahi, MA; Shivakumara, P; Idris, MYI; Anisi, MH; Blumenstein, M

A new image size reduction model for an efficient visual sensor network

Kaljahi, MA Shivakumara, P Idris, MYI Anisi, MH Blumenstein, M

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Visual Communication and Image Representation, 2019, 63
Issue Date:: 2019-08-01

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Field	Value	Language
dc.contributor.author	Kaljahi, MA
dc.contributor.author	Shivakumara, P
dc.contributor.author	Idris, MYI
dc.contributor.author	Anisi, MH
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2020-05-11T22:03:16Z
dc.date.available	2021-08-12T19:01:03Z
dc.date.issued	2019-08-01
dc.identifier.citation	Journal of Visual Communication and Image Representation, 2019, 63
dc.identifier.issn	1047-3203
dc.identifier.issn	1095-9076
dc.identifier.uri	http://hdl.handle.net/10453/140638
dc.description.abstract	© 2019 Elsevier Inc. Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Visual Communication and Image Representation
dc.relation.hasversion	Accepted Manuscript Version	en_US
dc.relation.isbasedon	10.1016/j.jvcir.2019.102573
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1203 Design Practice and Management, 1905 Visual Arts and Crafts
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A new image size reduction model for an efficient visual sensor network
dc.type	Journal Article
utslib.citation.volume	63
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1203 Design Practice and Management
utslib.for	1905 Visual Arts and Crafts
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1203 Design Practice and Management
utslib.for	1905 Visual Arts and Crafts
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-05-11T22:02:47Z
pubs.publication-status	Published
pubs.volume	63

Abstract:

© 2019 Elsevier Inc. Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.

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http://hdl.handle.net/10453/140638