Design and implementation of image compression using set partitioning in hierarchical trees and hybrid wavelet transform

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 495
DESIGN AND IMPLEMENTATION OF IMAGE COMPRESSION USING
SET PARTITIONING IN HIERARCHICAL TREES AND HYBRID
WAVELET TRANSFORM
Jyoti V. Kadam1
, Vijaykumar S. Kolkure2
1
M.E. (Electronics, Appeared), Department Of Electronics Engineering,Bharatratna Indira Gandhi College of
Engineering,Affiliated to Solapur University, Solapur, Maharashtra, India.
2
Assistant Professor, Department Of Electronics Engineering, Bharatratna Indira Gandhi College of Engineering,
Affiliated to Solapur University, Solapur, Maharashtra, India.
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size
of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or
some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we
present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT
implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis
is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms
because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of
compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher
compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
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1. INTRODUCTION
Image compression is storing images using lesser number of
bits than its original size. Image compression leads to less
storage space and less bandwidth for transmission. As
compression ratio increases, quality of reconstructed image
starts degrading. Many compression techniques are present
but transform based techniques are popular for image
compression. Discrete cosine transform (DCT) is widely
used. It separates an image into different frequency
components. Low frequency components are located at top
left corner. Elimination of these high frequency elements
gives transformed image with few low frequency
components. If image if image is reconstructed from such
lesser number of transformed low frequency elements, it
gives compressed image without losing much data contents in
original image. The DCT is fast. Discrete wavelet transform
(DWT) has gained widespread acceptance in signal
processing and image compression. The DWT is linear
transformation that operates on a data vector whose length is
an integer power of two, to transforming it into a numerically
different vector of same length. It is tool that separates data
into frequency components and then studies each component
with resolution matched to its scale. Set Partitioning in
Hierarchical Trees (SPIHT) is wavelet based image coder
that has many good characteristics like it gives good image
quality with a high value PSNR, fast coding and decoding
process. SPIHT can be used as lossless compression. Wavelet
transform coding is preferred over simple orthogonal
transform in image compression due to its multi resolution
property. It provides enhanced image quality even at higher
compression ratios. In this paper we use hybrid
transformation techniques which combine properties of two
transforms i.e. DWT and DCT.
2. PROPOSED SYSTEM
Figure 1 shows the block diagram of proposed system. The
input image used for this project is in different file formats
(.bmp, .png, .jpg, .tif). The wavelet transform is applied on
the original image i.e. image is subdivided into several
frequency bands to analyze image data. DWT using different
wavelets like Daubechies, Symlet and Biorthogonal is
applied. Hybrid wavelet transform is obtained by
combination of two orthogonal transform. Here, combination
of DWT and DCT is used. Wavelet transform of size N2xN2
is generated from NxN transform matrix. After forward
hybrid wavelet transform SPIHT encoder simply reduces the
number of bits needed to store the transformed coefficients
by reducing the precision of those values. SPIHT scans
wavelet coefficients along quad-tree instead of sub band.
SPIHT identifies significance only by the magnitude of
wavelet coefficients and encodes the sign separately into a
new bit stream. At the output of the encoder we get the
compressed image. Compressed image is given as an input to
the decoder. The decoder is a digital to analog converter
which performs exactly opposite operation of the encoder.
Here, inverse transform is applied on the decoded image
which is an exact opposite operation of the forward hybrid
wavelet transform. At the output of this stage we get the
original reconstructed image.

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Fig 1: Block Diagram of Proposed System
3. PERFORMANCE
The compression performance is measured through
parameters peak signal to noise ratio (PSNR), Mean Square
Error, Elapsed time. The mathematical formulae for the same
are
MSE = sum (sum ((Im- P) .^ 2))/(m*n)
PSNR= 10*log10 ((255*255)/MSE)
Where, Im is the original image, P is decoded image and m, n
are the dimensions of the image.
Elapsed time is the actual time between two events.
4. RESULT ANALYSIS
We have applied the hybrid (DWT, DCT) and SPIHT image
compression algorithm on several images and result are
shown in this section. Simulation is done by using MATLAB
software. Table1. shows the result of values of parameters
PSNR, MSE, Elapsed time for different images.
Table 1: Values of Different Parameters.
IMAGE PSNR MSE ELAPSED
TIME(seconds)
Lena 33.82 26.94 11.80
Barbara 33.25 30.71 13.68
Cameraman 33.41 29.64 12.35
Test Drive 32.77 34.33 14.21
5. CONCLUSION
In this paper we design and implement a hybrid DWT, DCT
and SPIHT algorithm for image compression. The
performance analysis on different images shows that SPIHT
and combination of DWT-DCT gives better result of image
compression as compared to separate DWT and DCT
techniques. Also these combination gives higher values of
PSNR and lower values of MSE.
REFERENCES
[1]. Rachana Dhannawat, Tanuja Sarode, H. B. Kekre,
“Kekre’s Hybrid Wavelet Transform Technique with DCT,
Walsh, Hartley and Kekre’s Transform for Image Fusion”,
International Journal of Computer Engineering and
Technology(IJCET), Vol. 4, Issue 1, Feb 2013, pp. 195-202.
[2]. JPEG2000 Image Coding System, document ISO/IEC
15444-1, 2000.
[3] .J. M. Shapiro, D. S. R. Center, and N. J. Princeton,
“Embedded image coding using zerotrees of wavelet
coefficients”, IEEE Signal Process. vol. 41, no. 12, pp.
34453462, Dec. 1993.
[4]. D. Taubman, “High performance scalable image
compression with EBCOT”, IEEE Trans. Image Process.,
vol. 9, no. 7, pp. 11581170, Jul. 2000.
[5]. A. Said and W. Pearlman, “A new, fast, and efficient
image codec based on set partitioning in hierarchical trees”,
IEEE Trans. Circuits Syst. Video Technol., vol. 6, no. 3, pp.
243250, Jun. 1996.
[6]. G. Pastuszak, “A novel architecture of arithmetic coder
in JPEG2000 based on parallel symbol encoding”, in Proc.
Int. Conf. Parallel Comput. Electr. Eng., 2004, pp. 303308.
[7]. Dr. H. B. Kekre, Tanuja Sarode, Prachi Natu,
“Performance Comparison of Hybrid Wavelet Transform
Formed by Combination of Different Base Transforms with
DCT on Image Compression”, International Journal of
Innovative Research in Science, Engineering and
Technology, Volume 3, Issue 1, January 2014.
[8]. Bhonde Nilesh, Shinde Sachin, Nagmode Pradip, D. B.
Rane, “Image compression Using Discrete Wavelet
Transform”, International Journal of Computer Technology
and Electronics Engineering (IJCTEE) Volume 3, Special
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[9]. Moh dAli Moustafa Alsayyh, Prof. Dr. Dzulkii
Mohamad, “Image Compression Using Hybrid Technique”,
Information and Knowledge Management ISSN 2224- 5758
(Paper) ISSN 2224-896X (Online)Vol 2, No.7, 2012.
[10]. J. Maly, P. Rajmic, “DWT-SPIHT Image Codec
Implementation”, IEEE Transactions, 2012, pp. 340-344. 44
[11]. Mr. M.B. Bhammar, Prof. K. A. Mehta, “Performance
Improvement Of SPIHT Algorithm Using Hybrid Image
BIOGRAPHIES
Ms. Jyoti Vishnu Kadam 1
currently
pursuing M.E. (Electronics) From
Bharatratna Indira Gandhi College of
Engineering, Solapur, Maharashtra, India.
Her area of interest is image processing.
Vijaykumar S. Kolkure 2
has completed M.E. (Electronics)
from W.C. Sangli, Maharashtra, India. He has 10 years of
teaching experience. Currently he is working as Assistant
Professor at Bharatratna Indira Gandhi College of
Engineering, Solapur, Maharashtra, India. His area of interest
is Image Processing, Video Processing.

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Section below shows the simulation result on Lena, Barbara, Cameraman and Test drive. For this purpose we have taken image
size of 256*256.

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Design and implementation of image compression using set partitioning in hierarchical trees and hybrid wavelet transform

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Design and implementation of image compression using set partitioning in hierarchical trees and hybrid wavelet transform