{"references": ["G. Nikolakopoulos, P. Stavrou, A. Tzes, D. Tsitsipis, D. Kandris, T.\nTheocharis, \"A dual scheme for compression and restoration of\nsequentially transmitted images over Wireless sensor networks\",\nElsevier Journal, Vol. 1, Issue1, January 2013, pp- 410-426.", "M. Mozammel Hoque, Amina, Khatun, \"Image Compression using\nDiscrete Wavelet Transform\", International Journal of Computer\nScience Issues (IJCSI), Vol. 9, No.1, Jul 2012, pp.621-627.", "Maneesha Gupta, Dr. Amit Kumar Jarg, \"Analysis of image\ncompression using Discrete Cosine Transform\", International Journal\nEngineering Research Applications (IJERA), Vol. 2, No.1, Jan-Feb\n2012, pp. 515- 521.", "Y. Wexler, E. Shechtman, M. Irani, \"Space-time video completion\",\nIEEE Transactions on Pattern Analysis and Machine Intelligence,\nVolume 29, No.3, 2007, pp 463\u2013476.", "W. Tan, A. Zakhor, \"Real-time Internet video using error resilient\nscalable compression and TCP friendly transport protocol\", IEEE\nTrans Multimedia, Vol. 1, Issue2, June 1999, p.p. 172- 186.", "J. De Bonet, \"Multi resolution Sampling Procedure for Analysis and\nSynthesis of Texture Images\", In Computer Graphics (SIGGRAPH'97\nConference, Proceeding), Aug 1997, pp- 361- 368.", "J. Vaisey, A. Gersho, \"Image compression with variable block size\nsegmentation\", IEEE Transaction on Signal processing, Vol. 40, p.p.\n2040- 2060, Aug 1992.", "G. J. Sullivan, R. L. Baker, \"Rate\u2013distortion optimization for tree\nstructured source coding with multi- way node decisions\", in Proc, IEEE\nInt.Conf.Acoust. Speech Signal Processing (ICASSP), pp III- 393-\n396, Mar 1992.", "G. J. Sullivan, \"Low-rate coding of moving images using motion\ncompensation, vector quantization and quad tree decomposition\" Ph.D.\nthesis, Univ.of Calif., Los Angles, Sept- 1991.\n[10] P. A. Chou, T. Lookabaugh, R. M. Gray, \"Optimal pruning with\napplications to tree- structured source coding and modeling\", IEEE\nTrans.Inform.Theory, Vol. 35, p.p. 299-315, Mar1989.\n[11] A. Wyner, J. Ziv, \"The rate-distortion function for source coding with\nside information at the decoder\", IEEE Trans. Inform. Theory 22(1), Jan\n1976, pp. 1-10."]}

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.