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

Kaljahi, Maryam Asadzadeh and Shivakumara, Palaiahnakote and Idris, Mohd Yamani Idna and Anisi, Mohammad Hossein and Blumenstein, Michael (2019) A new image size reduction model for an efficient visual sensor network. Journal of Visual Communication and Image Representation, 63. p. 102573. ISSN 1047-3203, DOI https://doi.org/10.1016/j.jvcir.2019.102573.

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Official URL: https://doi.org/10.1016/j.jvcir.2019.102573


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. © 2019 Elsevier Inc.

Item Type: Article
Funders: Faculty of Computer System and Information Technology at the University of Malaya under grant numbers: RP036B-15AET and PG063-2016 A
Uncontrolled Keywords: Visual sensor network; Image size reduction; Inter-redundancy; Intra-redundancy; Energy consumption; Quality of the image
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Apr 2020 05:06
Last Modified: 08 Apr 2020 05:06
URI: http://eprints.um.edu.my/id/eprint/24167

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