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Project Presentation

Design of an Efficient Architecture for 2-D Lifting-based

Discrete Wavelet Transform

S. GANGAIAH

ROLL NO: 08M21D7712

M.Tech in Embedded Systems and VLSI Design

Internal GuideSyed Amzad Ali

Professor .E.C.E,LIET

Head of the Department ,E.C.E

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AbstractThe digital data can be transformed using Discrete Wavelet Transform

(DWT). The images need to be transformed without loosing of information. The

Discrete Wavelet Transform (DWT) was based on time-scale representation, whichprovides efficient multi-resolution. The lifting based scheme(5, 3) (Here 5 Low Pass

filter coefficients and the 3 High Pass filter coefficients) filter 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 discrete wavelet transform (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 computationsand 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 work, the design of Lossless 2-D DWT (Discrete Wavelet

Transform) using Lifting Scheme Architecture will be modeled using the Verilog

HDL and its functionality were verified using the Modelsim tool and can besynthesized using the Xilinx tool.

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IntroductionWhy Discrete wavelet transform

The wavelet transform has gained widespread acceptance in signal processing andimage compression. Because of their inherent multi-resolution nature, wavelet-

coding schemes are especially suitable for applications where scalability andtolerable degradation are important

Applications of the project

Medical application

Signal de-noisingData compression

Image processing

What is an image?

An image (from Latin imago) or picture is an artifact, usually 2-Dimentional, thathas a similar appearance to some object or person

What is an image compression?

Image compression is minimizing the size in bytes of data without degrading thequality of the image to an acceptable level.

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Introduction (cont..)

There are two types of compressions

1.Lossless

Digitally identical to the original image. Only achieve a modest amount ofcompression

2.Lossy

Discards components of the signal that are known to be redundant. Signal istherefore changed from input

Lossless compression involves with compressing data, when decompressed datawill be an exact replica of the original data. This is the case when binary datasuch as executable are compressed.

What is wavelets?

Wavelet transform decomposes a signal into a set of basis functions. These basis

functions are called wavelets

What is Discrete wavelet transform?

Discrete wavelet transform (DWT), which transforms a discrete time signal to a

discrete wavelet representation.

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Literature Review

Lifting schema of DWT has been recognized as a faster approach

The basic principle is to factorize the poly-phase matrix of a wavelet filter

into a sequence of alternating upper and lower triangular matrices and a

diagonal matrix .

Figure 2 Image Analysis levels

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2-D DWT for Image

Figure 3: one level Image decomposition using DWT

Literature Review (cont..)

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Base Paper Explanation

Lifting scheme of discrete wavelet transform has been recognized as a

faster approach

Allows good localization both in time and spatial frequency domain

Transformation of the whole image introduces inherent scaling

Better identification of which data is relevant to human perception,

higher compression ratio

It is perceived that the wavelet transform is an important tool for

analysis and processing of signals and Images. The wavelet transform in

its continuous form can accurately represent minor variations in signal or

Image characteristics. Critically sampled version of continuous wavelet

transform, known as standard DWT

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DWT is very popular for de-noising and compression in a number

of applications by the virtue of its computational simplicity through

fast algorithms, and non-redundant. There are certain signal

processing applications (e.g. Time-division multiplexing in

Telecommunication)

Base Paper Explanation

Signal Analysis and Reconstruction in DWT

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Generalized Architecture for DWT

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Tools (H/W and S/W)/technology used

MODELSIM SimulationsXILINX-ISE Synthesis

ModelSim is a verification and simulation tool for VHDL, Verilog,

SystemVerilog, and mixed language designs.

The following diagram shows the basic steps for simulating a design

in ModelSim.

In ModelSim, all designs are compiled into a library. You typically

start a new simulation in ModelSim by creating a working librarycalled "work". "Work" is the library name used by the compiler as

the default destination for compiled design units.

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After creating the working library, you compile your design

units into it. The ModelSim library format is compatible across all

supported platforms. You can simulate your design on anyplatform without having to recompile your design.

Loading the Simulator with Your Design and Running the

Simulation With the design compiled, you load the simulator with

your design by invoking the simulator on a top-level module

(Verilog) or a configuration or entity/architecture pair (VHDL).Assuming the design loads successfully, the simulation time is set

to zero, and you enter a run command to begin simulation.

Debugging Your Results :

If you dont get the results you expect, you can useModelSims robust debugging environment to track down the

cause of the problem.

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Modelsim Procedure

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Xilinx-ISE Procedure

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1. Create an ISE PROJECT for particular systemapplication.

2. Write the assembly code in notepad or write pad and

generate the verilog or vhdl module by making use

of assembler.

3. Check syntax for the design.

4. Create verilog test fixture of the design.

5. Simulate the test bench waveform (BEHAVIORAL

SIMULATION) for functional verification of the

design using ISE simulator.

6. Synthesize and implement the top level module

using XST synthesizer.

Design steps using Xilinx ISE 10.1

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Applications of Discrete Wavelet Transform

Medical applicationSignal denoising

Data compression

Image processing

Future scope of the Work:As future work,

This work can be extended in order to increase the accuracy by

increasing the level of transformations.

This can be used as a part of the block in the full fledged

application, i.e., by using these DWT, the applications can be

developed such as compression, watermarking, etc.

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THANK YOU