NDLI: Comparison of encoding techniques for transmission of image data obtained using compressed sensing in wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Loganathan, A. Hemalatha, R. Radha, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. & Commun., SSN Coll. of Eng., Chennai, India (Loganathan, A.; Hemalatha, R.; Radha, S.)
Abstract	The Wireless Sensor Network (WSN) has limitations in bandwidth and computational resources as they have limited communication and storage capabilities. WSN consists of cameras, which have some local image processing and one or more central computers, where image data from multiple cameras is further processed and fused. Because of these limitations, the encoding techniques used for transmitting the image data should be efficient in order to make use of the available resources properly. A new sampling method is also 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 by applying 2 levels of wavelet transform. The dense component uses the standard encoding procedure such as JPEG and the sparse measurements obtained from the sparse components are encoded by the techniques such as Exponential Golomb coding followed by Run-length encoding and arithmetic coding and the performances in terms of compression ratio and bits per pixel are compared. The recovery algorithm may be anyone supporting the compressed sensing technique such as OMP, POCS etc. In this work, the measurements (used in CS) and the predicted sparse components as the initial values, the projection onto convex set (POCS) recovery algorithm is used to get back the original sparse components of two levels and hence the original image by applying the inverse of transform to the dense and recovered sparse components.
Sponsorship	IEEE Madras Sect.
Starting Page	696
Ending Page	701
File Size	414052
Page Count	6
File Format	PDF
ISBN	9781479910243
DOI	10.1109/ICRTIT.2013.6844285
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Compressed Sensing Encoding JPEG Sparse matrices Projection onto Convex set PAR model based interpolation Interpolation Image coding Transform coding Exponential Golomb coding Arithmetic coding Image interpolation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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