Quad Tree Decomposition Based Analysis of Compressed Image Data Communication for Lossy and Lossless Using WSN
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Quad Tree Decomposition Based Analysis of Compressed Image Data Communication for Lossy and Lossless Using WSN

Authors: N. Muthukumaran, R. Ravi

Abstract:

The Quad Tree Decomposition based performance analysis of compressed image data communication for lossy and lossless through wireless sensor network is presented. Images have considerably higher storage requirement than text. While transmitting a multimedia content there is chance of the packets being dropped due to noise and interference. At the receiver end the packets that carry valuable information might be damaged or lost due to noise, interference and congestion. In order to avoid the valuable information from being dropped various retransmission schemes have been proposed. In this proposed scheme QTD is used. QTD is an image segmentation method that divides the image into homogeneous areas. In this proposed scheme involves analysis of parameters such as compression ratio, peak signal to noise ratio, mean square error, bits per pixel in compressed image and analysis of difficulties during data packet communication in Wireless Sensor Networks. By considering the above, this paper is to use the QTD to improve the compression ratio as well as visual quality and the algorithm in MATLAB 7.1 and NS2 Simulator software tool.

Keywords: Image compression, Compression Ratio, Quad tree decomposition, Wireless sensor networks, NS2 simulator.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097447

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References:


[1] G. Nikolakopoulos, P. Stavrou, A. Tzes, D. Tsitsipis, D. Kandris, T. Theocharis, “A dual scheme for compression and restoration of sequentially transmitted images over Wireless sensor networks”, Elsevier Journal, Vol. 1, Issue1, January 2013, pp- 410-426.
[2] M. Mozammel Hoque, Amina, Khatun, “Image Compression using Discrete Wavelet Transform”, International Journal of Computer Science Issues (IJCSI), Vol. 9, No.1, Jul 2012, pp.621-627.
[3] Maneesha Gupta, Dr. Amit Kumar Jarg, “Analysis of image compression using Discrete Cosine Transform”, International Journal Engineering Research Applications (IJERA), Vol. 2, No.1, Jan-Feb 2012, pp. 515- 521.
[4] Y. Wexler, E. Shechtman, M. Irani, “Space-time video completion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, No.3, 2007, pp 463–476.
[5] W. Tan, A. Zakhor, “Real-time Internet video using error resilient scalable compression and TCP friendly transport protocol”, IEEE Trans Multimedia, Vol. 1, Issue2, June 1999, p.p. 172- 186.
[6] J. De Bonet, “Multi resolution Sampling Procedure for Analysis and Synthesis of Texture Images”, In Computer Graphics (SIGGRAPH’97 Conference, Proceeding), Aug 1997, pp- 361- 368.
[7] J. Vaisey, A. Gersho, “Image compression with variable block size segmentation”, IEEE Transaction on Signal processing, Vol. 40, p.p. 2040- 2060, Aug 1992.
[8] G. J. Sullivan, R. L. Baker, “Rate–distortion optimization for tree structured source coding with multi- way node decisions”, in Proc, IEEE Int.Conf.Acoust. Speech Signal Processing (ICASSP), pp III- 393- 396, Mar 1992.
[9] G. J. Sullivan, “Low-rate coding of moving images using motion compensation, vector quantization and quad tree decomposition” Ph.D. thesis, Univ.of Calif., Los Angles, Sept- 1991.
[10] P. A. Chou, T. Lookabaugh, R. M. Gray, “Optimal pruning with applications to tree- structured source coding and modeling”, IEEE Trans.Inform.Theory, Vol. 35, p.p. 299-315, Mar1989.
[11] A. Wyner, J. Ziv, “The rate-distortion function for source coding with side information at the decoder”, IEEE Trans. Inform. Theory 22(1), Jan 1976, pp. 1-10.