Bonfring International Journal of Advances in Image Processing, Vol. 1, Special Issue, December 2011 6 ISSN 2250 – 1053 | © 2011 Bonfring Abstract--- In this paper, the digital data can be transformed using Discrete Wavelet Transform. The images need to be transformed without loosing of information. The Discrete Wavelet Transform was based on time-scale representation, which provides efficient multi-resolution. The lifting based scheme (5, 3) filters give lossless mode of information as per the JPEG 2000 Standard. The lifting based DWT are lower computational complexity and reduced memory requirements. Since Conventional convolution based DWT is area and power hungry which can be overcome by using the lifting based scheme. The DWT is being increasingly used for image coding. This is due to the fact that DWT supports features like progressive image transmission (by quality, by resolution), ease of transformed image manipulation, region of interest coding, etc. DWT has traditionally been implemented by convolution. Such an implementation demands both a large number of computations and a large storage features that are not desirable for either high-speed or low-power applications. Recently, a lifting- based scheme that often requires far fewer computations has been proposed for the DWT. In this paper, the design of Lossless 2-D DWT using Lifting Scheme Architecture will be modeled using the Verilog HDL and its functionality were verified using the Modelsim tool and can be synthesized using the Xilinx tool. Keywords--- Fourier Transform (FT), Short Time FT (STFT), Discrete Wavelet Transform (STFT), Multi-resolution Analysis (MRA). I. INTRODUCTION HE fundamental idea behind wavelets is to analyze according to scale. Indeed, some researchers in the wavelet field feel that, by using wavelets, one is adopting a perspective in processing data. Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. This idea is not new. Approximation using superposition of functions has existed since the early 1800's, when Joseph Fourier discovered that he could superpose sines and cosines to represent other functions. However, in wavelet analysis, the scale that we use to look at data plays a special role. Wavelet algorithms process data at S. Suresh, Assistant Professor, Department of ECE, Don Bosco Institute of Tech & Science K. Rajasekhar, Assistant Professor, Department of ECE, Narasaraopeta Engineering College M. Venugopal Rao, Professor and HOD, Narasaraopeta Engineering College. Dr. Bv Rammohan Rao, Professor and Principal, Narasaraopeta Engineering College. R. Samba Siva Nayak, Professor and HOD, Don Bosco Institute of Tech & Science, E-mail: sambanayak@gmail.com different scales or resolutions. FT with its fast algorithms (FFT) is an important tool for analysis and processing of many natural signals. FT has certain limitations to characterize many natural signals, which are non-stationary (e.g. speech). Though a time varying, overlapping window based FT namely STFT is well known for speech processing applications, a time-scale based Wavelet Transform is a powerful mathematical tool for non-stationary signals. Wavelet Transform uses a set of damped oscillating functions known as wavelet basis. WT in its continuous (analog) form is represented as CWT. CWT with various deterministic or non-deterministic basis is a more effective representation of signals for analysis as well as characterization. Continuous wavelet transform is powerful in singularity detection. A discrete and fast implementation of CWT is known as the standard DWT.With standard DWT, signal has a same data size in transform domain and therefore it is a non-redundant transform. A very important property was Multi-resolution Analysis allows DWT to view and process. II. DWT The discrete wavelet transform became a very versatile signal processing tool after Mallat proposed the multi- resolution representation of signals based on wavelet decomposition. The method of multi-resolution is to represent a function (signal) with a collection of coefficients, each of which provides information about the position as well as the frequency of the signal (function). The advantage of the DWT over Fourier transformation is that it performs multi-resolution analysis of signals with localization both in time and frequency, popularly known as time-frequency localization. As a result, the DWT decomposes a digital signal into different sub bands so that the lower frequency sub bands have finer frequency resolution and coarser time resolution compared to the higher frequency sub bands. The DWT is being increasingly used for image compression due to the fact that the DWT supports features like progressive image transmission, ease of compressed image manipulation region of interest coding, etc. Because of these characteristics, the DWT is the basis of the new JPEG2000 image compression standard. 1-D DWT: Any signal is first applied to a pair of low-pass and high-pass filters. Then down is applied to these filtered coefficients. The filter pair (h, g) which is used for decomposition is called analysis filter-bank and the filter pair which is used for reconstruction of the signal is called synthesis filter bank.(g`, h`).The output of the low pass filter after down sampling contains low frequency components of the signal which is approximate part of the original signal and Three-D DWT of Efficient Architecture S. Suresh, K. Rajasekhar, M. Venugopal Rao, Dr.B.V. Rammohan Rao and R. Samba Siva Nayak T