Compressed Sensing Based Image Encoding Technique for Wireless Sensor Networks

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2014 by IJPTT Journal
	Volume - 4 Issue - 2
	Year of Publication : 2014
	Authors : A. Loganathan , S.Harish , Dr. R.Kanthavel

Citation

A. Loganathan , S.Harish , Dr. R.Kanthavel."Compressed Sensing Based Image Encoding Technique for Wireless Sensor Networks ". International Journal of P2P Network Trends and Technology (IJPTT), V4(2):23-29 Mar - Apr 2014, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

The Wireless Sensor Network (WSN) is the one, which generally consists of cameras themselves, which have some local image processing, communication and storage capabilities, and one or more central computers, where image data from multiple cameras is further processed and fused. Here the computation resource is extremely limited. Because of these limitations, a new sampling method is introduced in the Image/video encoder of the WSN called Compressed Sensing (CS), which is the process of acquiring and reconstructing a signal that is supposed to be sparse or compressible, thus reducing the computational complexity. The image is divided into dense and sparse components [1], where the dense component uses the standard encoding procedure such as JPEG and the sparse measurements from the sparse components are encoded by the Arithmetic encoding. The correlation between the dense and sparse components is studied using the autoregressive model, by which the sparse components are predicted from the dense component at the receiver side. With the measurements (used in CS) and the predicted sparse components as the initial values, the projection onto convex set (POCS) recovery algorithm [2] is used to get back the original sparse components and hence the original image by applying the inverse of transform to the dense and recovered sparse components.

References

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Keywords

Compressed Sensing, JPEG, Arithmetic encoding, Image interpolation;PAR model, Variable adaptive interpolation, Projection onto Convex set.