Wavelet based image compression technique
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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
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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
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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.
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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.
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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 .
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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.
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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.
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Image
compression
techniques
Lossless
H
Huffman coding
Run length encoding
LZW encoding , etc
Lossy
Transformation coding
Vector coding
Fractal coding , etc
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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.
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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.
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Source encoder
Thresholder
Quantizer
Entropy encoder
Source image
Compressed image
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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.
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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.
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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.
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WAVELET IMAGE COMPRESSION EXPLAINED USING LENNA IMAGE
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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.
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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.
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OTHER COMPRESSION
METHODS
GIF
PNG
BMPJPEG
2000
JPEG
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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.
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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.
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Quality progressive or layer progressive.
Resolution progressive.
Region of interest coding.
Meta information
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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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1.Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, Feb-March 2001 .
2.Sonal & Dinesh Kumar ,”A Study Of Various Image Compression Technique”.International Journal Of Computer Science,Vol. 20 No. 3, Dec 2003, pp. 50-55.
3. Grossmann, A. and Morlet, J. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal of Analysis,15: 723-736, 1984.
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
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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6. N. Ahmed, T. Natarjan, “Discrete Cosine Transforms ”. IEEE Trans. Computers, C-23, 1974, pp. 90-93.
7. Sonja Grgic, Mislav Grgic, & Branka Zovko-Cihlar, “Performance Analysis of Image Compression Using Wavelets”, IEEE Transaction On Industrial Electronics, Vol. 48, No. 3, June 2001
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.
9. C. Christopoulos, A. Skodras, and T.Ebrahimi, The JPEG2000 Still Image Coding System: An Overview, IEEE Trans. On Consumer Electronics, Vol.46, No.4, November 2000, 1103-1127.
10. David H. Kil and Fances Bongjoo Shin, “ Reduced Dimension Image Compression And its Applications,”Image Processing, 1995, Proceedings, International Conference,Vol. 3 , pp 500-503, 23-26 Oct.,1995.
11. C.K. Li and H.Yuen, “A High Performance Image Compression Technique for Multimedia Applications,” IEEE Transactions on Consumer Electronics, Vol. 42, no. 2, pp 239-243, 2 May 1996.
12. Ming Yang & Nikolaos Bourbakis ,“An Overview of Lossless Digital Image Compression Techniques and Its Application,Circuits & Systems, vol 2 .IEEE ,10 Aug, 2005.
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