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. Image compression, Compression Ratio, Quad tree decomposition, Wireless sensor networks, NS2 simulator. I. INTRODUCTION HIS document is, currently wireless sensor networks are beginning to be deployed at an accelerated pace. It won’t be too far that the world will be covered with wireless sensor networks with access to them via the internet [1]. Recent technological advances have enabled the development of sophisticated wireless sensor nodes which have the ability to perform sensing, processing, communication and actuation tasks. A wireless sensor network is a collection of nodes organized into a cooperative network [1]. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single Omni8directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. QTD can be applied through two alternative approaches; the first is Muthukumaran.N, Assistant Professor, is with the Department of Electronics and Communication Engineering, 103/G2, Bypass Road, Francis Xavier Engineering College, Vannarpettai, Tirunelveli, Tamilnadu, India 8 627003, (Phone: +91 462 2502283, +91 9952203887; Fax: +91 462 2501007; e8mail: kumaranece@gmail.com). Ravi Ramraj is Professor and Head, Department of Computer Science and Engineering, 103/G2, Bypass Road, Francis Xavier Engineering College, Vannarpettai, Tirunelveli, Tamilnadu, India 8 627003, (Phone: +91 462 2502283, +91 9442418917; Fax: +91 462 2501007; e8mail: fxhodcsf@gmail.com). bottom8up decomposition and second is top8down decomposition. However the top8down decomposition is considered to outperform bottom8up QTD for images. QTD has been widely used for its low complexity and powerful compression potential [8], [9]. II.PROBLEM FORMULATION Various techniques are used for image compression which includes discrete cosine transform, fractal compression, JPEG and discrete wavelet transform. Among these DCT, fractal compression is most commonly used technique. The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components [3]. Fractal compression is a lossy compression and it requires more computational complexity. Thus the Image compression is involved by the Discrete Wavelet Transform for which the wavelets are discretely sampled. The algorithms have been implemented using visual C++. Here the Peak Signal to Noise Ratio is poor [2], [3]. III. PROBLEM SOLUTION     Most of the coding methods are based on image decomposition. Typically a natural image consists of several regions that possess local similarity and others that have extensively varying content. In this proposed scheme QTD is used. QTD is an image segmentation method that divides the image into homogeneous areas. There are various compression methods, the most popular being discrete cosine transform, fractal compression and discrete wavelet transform [5], [6]. The aforementioned methods are mathematically complex and time consuming. Thus to overcome the above mentioned problem QTD algorithm is used.QTD has a powerful compression potential. The QTD can be applied in two alternative approaches [8], Bottom8Up QTD and Top8Down QTD. The compression performed in this work is Top8Down QTD. The image compression performed in this work, is based on the Top–Down QTD method. In Top–Down decomposition, each image is initially divided into four blocks of equal size. Next, each of the newly generated blocks recursively splits into four new blocks if it is in homogeneous and its size is greater than the minimum allowable block size [4], [7]. In general, in terms of processing speed, the Top–down QTD is considered to outperform Bottom–Up QTD for images which Quad Tree Decomposition Based Analysis of Compressed Image Data Communication for Lossy and Lossless Using WSN N. Muthukumaran, R. Ravi T World Academy of Science, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering Vol:8 No:9, 2014 1543 International Scholarly and Scientific Research & Innovation 8(9) 2014 International Science Index Vol:8, No:9, 2014 waset.org/Publication/10000023