National Conference on “Recent Trends on Soft
Computing and Computer Network”
PROF. ARPITA BARONIA
PROF. ALEKH DWIVEDI
PROF. RATNESH DUBEY
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
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
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
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.
Rajesh K. Yadav, S.P. Gangwar & Harsh V. Singh :
• Study and analysis of wavelet based image compression
• 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
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 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
Digital images usually require a
very large number of bits, this
causes critical problem for
digital image data transmission
It is the Art & Science of
reducing the amount of data
required to represent an image.
It is one of the most useful and
technologies in the field of
Digital Image Processing.
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.
Aims at removing duplication from the signal
Omits the part of signal that will not be noticed
by the signal receiver.
Digitize the source image to a signal s, which is
a string of numbers.
Decompose the signal into a sequence of wavelet
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
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.
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
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
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
Format Name Compression
4:1-10:1 Lossless for flat
color sharp edged
art or text
10:1-100:1 Best suited for
Lossless for flat-
ratio, better image
Biology for cell membrane recognition, to
distinguish the normal from the pathological
DNA analysis, protein analysis.
Computer graphics ,multimedia and multifractal
Quality progressive or layer progressive.
Region of interest coding.
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 techniques currently provide the most promising approach to
high-quality image compression, which is essential for many real world
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