PRESENTED BY:PRIYANKA PACHORI

SHREYA PIPADAV-SEM, CSE

LNCT,BHOPAL

National Conference on “Recent Trends on Soft

Computing and Computer Network”

GUIDED BY:PROF. ARPITA BARONIAPROF. ALEKH DWIVEDIPROF. RATNESH DUBEY

INTRODUCTION

LITERATURE REVIEW

WHY IMAGE COMPRESSION ?

IMAGE COMPRESSION TECHNIQUES

WAVELET BASED IMAGE COMPRESSION

WAVELET TRANSFORM V/S FOURIER TRANSFORM

COMPARISION WITH OTHER METHODS

ADVANTAGES OF USING WAVELET TRANSFORM IN IMAGE COMPRESSION

APPLICATIONS

CONCLUSION

Digital imaging has an enormous impact on scientific and industrial applications. There is always a need for greater emphasis on image storage, transmission and handling. Before storing and transmitting the images, it is required to compress them, because of limited storage capacity and bandwidth.

Wavelets decompose complex information such as music, images, videos and patterns into elementary forms.

compression techniques: lossy and lossless.

Comparison of wavelet transform with JPEG, GIF, and PNG are outlined to emphasize the results of this compression system.

Sonja Grgic , Mislav Grgic , & Branka Zovko-Cihlar :

• Compared different image compression techni- rhghghvques such as GIF,PNG,JPEG and DWT.

Amhamed Saffor, Abdul Rahman Ramli & Kwan-Hoong Ng :

• Performed a Comparative Study Of Image Compression.

• Compared wavelet with the formal compression standard “Joint Photographic Expert Group” JPEG, using JPEG Wizard.

M. Sifuzzaman1, M.R. Islam1 and M.Z. Ali 2 :

• Application of Wavelet Transform and its Advantages.

• Comparison of wavelet transform with Fourier Transform.

Rajesh K. Yadav, S.P. Gangwar & Harsh V. Singh :• Study and analysis of wavelet based image compression

techniques.• The goals of image compression are to minimize the

storage requirement and communication bandwidth.

Sonal and Dinesh Kumar :• Studied various image compression techniques.• Includes various benefits of using image compression

techniques.

Dr. Jyoti Sarup, Dr. Jyoti Bharti Arpita Baronia :• There could be a decrease in image quality with

compression ratio increase. • Wavelet-based compression provides substantial

improvement in picture quality .

Digital Image

Digital Image Processing

It refers to processing digital images by means of a digital computer.

The digital image is composed of a finite number of elements, each of

which has a particular location and values. These elements are referred

to as picture elements, image elements and pixels.

An image is a two-dimensional function, f(x,

y), where x and y are spatial coordinates. When

x, y and the amplitude values of f are all finite,

discrete quantities, we call the image a digital

image.

Digital images usually require a very large number of bits, this causes critical problem for digital image data transmission and storage.

It is the Art & Science of reducing the amount of data required to represent an image.

It is one of the most useful and commercially successful technologies in the field of Digital Image Processing.

Image

compression

techniques

Lossless

H

Huffman coding

Run length encoding

LZW encoding , etc

Lossy

Transformation coding

Vector coding

Fractal coding , etc

What are wavelets?

Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale.

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.

REDUNDANCY REDUCTION

Aims at removing duplication from the signal source (image/video).

IRRELEVANCY REDUCTION

Omits the part of signal that will not be noticed by the signal receiver.

Source encoder

Thresholder

Quantizer

Entropy encoder

Source image

Compressed image

Digitize the source image to a signal s, which is

a string of numbers.

Decompose the signal into a sequence of wavelet

coefficients.

Use Thresholding to modify the wavelet

compression from w, to another sequence w’.

Use Quantization to convert w’ to a sequence q.

Apply Entropy coding to compress q into a

sequence e.

Wavelet transform of a function is the improved versionof Fourier transform.

Fourier transform is a powerful tool for analyzing thecomponents of a stationary signal but it is failed foranalyzing the non-stationary signals whereas wavelettransform allows the components of a non-stationarysignal to be analyzed.

The main difference is that wavelets are well localized inboth time and frequency domain whereas the standardFourier transform is only localized in frequency domain.

Wavelet transform is a reliable and better techniquethan that of Fourier transform technique.

Transformation of spatial information into frequency domain.

The transformed image is quantized i.e. when some data samples usually those with insignificant energy levels are discarded.

Entropy coding minimizes the redundancy in the bit stream and is fully invertible at the decoding end.

The inverse transform reconstructs the compressed image in the spatial domain.

WAVELET IMAGE COMPRESSION EXPLAINED USING LENNA IMAGE

The advantage of wavelet compression is

that, in contrast to JPEG, wavelet algorithm does

not divide image into blocks, but analyze the whole

image.

Wavelet transform is applied to sub images, so it

produces no blocking artifacts.

Wavelets have the great advantage of being able to separate

the fine details in a signal.

Very small wavelets can be used to isolate very fine details in

a signal, while very large wavelets can identify coarse details.

These characteristic of wavelet compression allows getting

best compression ratio, while maintaining the quality of the

images.

OTHER COMPRESSION

METHODS

GIF

PNG

BMPJPEG

2000

JPEG

Format Name Compression ratio

Description

GIF Graphics Interchange

Format

4:1-10:1 Lossless for flat color sharp edged

art or text

JPEG Joint Photographic Experts group

10:1-100:1 Best suited for continuous tone

images

PNG Portable Network Graphics

10-30% smaller than

GIFs

Lossless for flat-color, sharp-edged

art.

DWT Discrete Wavelet

Transform

30-300% greater than

JPEG, or 600:1 in general

High compression ratio, better image

quality without much loss.

Fingerprint verification.

Biology for cell membrane recognition, to

distinguish the normal from the pathological

membranes.

DNA analysis, protein analysis.

Computer graphics ,multimedia and multifractal

analysis.

Quality progressive or layer progressive.

Resolution progressive.

Region of interest coding.

Meta information

These image compression techniques are basically classified into Lossy and

lossless compression technique.

Image compression using wavelet transforms results in an improved compression

ratio as well as image quality.

Wavelet transform is the only method that provides both spatial and frequency

domain information. These properties of wavelet transform greatly help in

identification and selection of significant and non-significant coefficient amongst

wavelet transform.

Wavelet transform techniques currently provide the most promising approach to

high-quality image compression, which is essential for many real world

applications.

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4. Amhamed Saffor, Abdul Rahman Ramli & Kwan-Hoong Ng ,” A Comparitive Study Of Image Compression Between JPEG And Wavelet”. Malaysian Journal of Computer Science, Vol. 14 No. 1, June 2001, pp. 39-45

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8. M. Sifuzzaman & M.R. Islam1 and M.Z. Ali ,” Application of Wavelet Transform and its Advantages Compared to Fourier Transform” Journal of Physical Sciences, Vol. 13, 2009, 121-134.

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