1. PRESENTED BY:
PRIYANKA PACHORI
SHREYA PIPADA
V-SEM, CSE
LNCT,BHOPAL
National Conference on “Recent Trends on Soft
Computing and Computer Network”
GUIDED BY:
PROF. ARPITA BARONIA
PROF. ALEKH DWIVEDI
PROF. RATNESH DUBEY

2.  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

3.  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.

4.  Sonja Grgic , Mislav Grgic , & Branka Zovko-Cihlar :
• Compared different image compression techni- rhghghv
ques 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.

5.  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 .

6.  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.

7.  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.

8. Image
compression
techniques
Lossless
H
Huffman coding
Run length encoding
LZW encoding , etc
Lossy
Transformation coding
Vector coding
Fractal coding , etc

9. 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.

10.  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.

11. Source encoder
Thresholder
Quantizer
Entropy encoder
Source image
Compressed
image

12.  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.

13.  Wavelet transform of a function is the improved version
of Fourier transform.
 Fourier transform is a powerful tool for analyzing the
components of a stationary signal but it is failed for
analyzing the non-stationary signals whereas wavelet
transform allows the components of a non-stationary
signal to be analyzed.
 The main difference is that wavelets are well localized in
both time and frequency domain whereas the standard
Fourier transform is only localized in frequency domain.
 Wavelet transform is a reliable and better technique
than that of Fourier transform technique.

14.  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.

15. WAVELET IMAGE COMPRESSION EXPLAINED
USING LENNA IMAGE

16.  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.

17.  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.

18. OTHER
COMPRESSION
METHODS
GIF
PNG
BMP
JPEG
2000
JPEG

19. 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.

20.  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.

21.  Quality progressive or layer progressive.
 Resolution progressive.
 Region of interest coding.
 Meta information

23.  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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Technique”.International Journal Of Computer Science,Vol. 20 No. 3, Dec
2003, pp. 50-55.
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Comparitive Study Of Image Compression Between JPEG And Wavelet”.
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39-45
 5. Rajesh K. Yadav, S.P. Gangwar & Harsh V. Singh,” Study and analysis
of wavelet based image compression techniques. International Journal of
Engineering, Science and Technology,Vol. 4, No. 1, 2012, pp. 1-7

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