A low-overhead adaptive image compression technique for energy-constrained WMSN. (August 2018)

• Record Type: Journal Article
• Authors: Pal, Tamal; DasBit, Sipra
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 594
• Page End: 615
• Publication Date: 2018-08
• Subjects: Wireless multimedia sensor network -- Image segmentation -- Colour filter array -- Shape matrix -- PSNR -- Cooja simulator; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.09.006
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Concept of 'rough segment' is introduced based on image segmentation and set theory. Multilayered template is generated off-line using the proposed rough segment. Based on the above template image segmentation is performed. Image is compressed by dropping columns of image pixel array adaptively to save energy. Comparative performance of the proposed scheme is evaluated both through qualitative and quantitative analyses. Abstract: Advancement of low-cost hardware capable of processing multimedia data coupled with efficient short range radio communication has led to the development of Wireless multimedia sensor network (WMSN). We consider post-disaster situation analysis application where the nodes in a WMSN capture disaster images from the affected area. Energy is one of the scarcest resources for such a network especially for disaster application where power supply for charging the battery of a node is nearly impossible. In this paper we exploit such facts and propose an energy saving multimedia (image) data processing scheme for its subsequent transmission over energy-constrained WMSN. Unlike conventional image processing, it saves energy by reducing the data to be transmitted at the cost of segmentation-less adaptive compression. Finally, the comparative performance of the scheme shows its dominance over all the competing schemes. Precisely, our scheme saves on an average 34% energy over all the competing schemes. Graphical abstract: