IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 2, Issue 4 (May. – Jun. 2013), PP 14-20 e-ISSN: 2319 – 4200, p-ISSN No. : 2319 – 4197 www.iosrjournals.org www.iosrjournals.org 14 | Page Image Reconstruction Using Discrete Wavelet Transform G.Shruthi 1 , Radha Krishna A.N. 2 Assistant Professor, CSE Dept 1 , ECE Dept 2 Avanthi’s Scientific Technological & Research Academy, Hyderabad, Andhra Pradesh, India. Abstract : In the recent growth of data intensive and multimedia based applications, efficient image compression solutions are becoming critical. The main objective of Image Compression is to reduce redundancy of the data and improve the efficiency. The main techniques used are Fourier Analysis, Discrete Cosine Transform vector quantization method, sub-band coding method. The drawbacks in the above methods are, they cannot be used for real time systems. In order to overcome these problems, the Wavelet Transform method has been introduced. Wavelet Analysis is highly capable of revealing aspects of data like trends, breakdown points, discontinuities in higher derivates and self similarity and can often compress or diagnose a signal without appreciable degradation. Here, we implement a lossy image compression technique using Matlab Wavelet Toolbox and Matlab Functions where the wavelet transform of the signal is performed, then calculated a threshold based on the compression ratio acquired by the user. Keywords : CWT, DWT, Decomposition,, Haar Transform, Lossy Compression, Wavelet. I. INTRODUCTION In the last decade, there has been a lot of technological transformation in the way of communication. This transformation includes the ever present, ever growing internet, the explosive development in mobile communication and ever increasing importance of video communication. Data Compression is one of the technologies for each of the aspect of this multimedia revolution. Portable Devices would not be able to provide communication with increasing clarity, without data compression. Data compression is art and science of representing information in compact form. Uncompressed multimedia data requires considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds, and digital communication system performance, demand for data storage capacity and data-transmission bandwidth continues to outstrip the capabilities of available technologies. In a distributed environment large image files remain a major bottleneck within systems. Image Compression is an important component of the solutions available for creating image file sizes of manageable and transmittable dimensions. Platform portability and performance are important in the selection of the compression/decompression technique to be employed. The basic model of image compression is shown in figure 1.1. Figure 1.1: Block diagram of image compression II. Image Compression Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.