International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
Enhancement of Image Compression in Neural Network Using Wavelets
Divya Prashar1,Dr.Archana Kumar2
1
2
Mtech Student,Dept.of CSE,Delhi Institute of technology
and Management,DCRUST Murthal India
Assosciate Professor,Dept. of CSE, Delhi Institute of technology
and Management,DCRUST Murthal India
Abstract— Image compression is the technique of reducing
the size of image file without degrading the quality of the
image Bandwidth conservation is an important issue in case
of multimedia communication. Uncompressed multimedia
(graphical, audio and video) data requires considerable
storage capacity and transmission bandwidth. It demands for
data storage capacity and data-transmission bandwidth
continuously to outstrip the capabilities of available
technologies. So to solve this problem an efficient
multimedia communication scheme is proposed which is
based on Wavelet. This paper shows the idea of image
compression based on hierarchical back propagation neural
network and results are analyzed The further analysis is
conducted in the network model and tested training
algorithm. This concludes that a high compression ratio is
achieved with Bi-orthogonal Wavelet functions. The results
are obtained with a Bi-orthogonal 6.8 Reconstruction
Wavelet function and proved the best. Then Neural Network
is implemented to prove the best result and hence achieved.
Experimental results suggest that the proposed system can
be efficiently used to compress while maintaining high
image compression.
Keywords-magecompression,wavelet,BackPropogation,
Neural network,Radiograph
I. INTRODUCTION.
Uncompressed multimedia (graphics, audio and video) data
requires considerable storage capacity and transmission
bandwidth. To enable modern high bandwidth required in
wireless data services such as mobile multimedia, email,
mobile, internet access, mobile commerce, mobile data
sensing in sensor networks, home and medical monitoring
services and mobile conferencing, there is a growing demand
for rich content cellular data communication, including
Voice,Text, Image and Video.
One of the major challenges in enabling mobile multimedia
data services will be the need to process and wirelessly
transmit very large volume of this rich content data. This will
impose severe demands on the battery resources of
multimedia mobile appliances as well as the bandwidth of the
wireless network[1]. While significant improvements in
achievable bandwidth are expected with future wireless access
technology, improvements in battery technology will lag the
ISSN: 2231-5381
rapidly growing energy requirements of the future wireless
data services. One approach to mitigate this problem is to
reduce the volume of multimedia data transmitted over the
wireless channel via data compression technique such as
JPEG, JPEG2000 and MPEG.
1.1Need Of Compression
In the last decade, there has been a lot of technological
transformation in the way we communicate. This
transformation includes the ever present, ever growing
internet, the explosive development in mobile communication
and ever increasing importance of video communication.
Data Compression is one of the technologies for each of the
aspect of this multimedia revolution. Cellular phones would
not be able to provide communication with increasing clarity
without data compression. Data compression is art and science
of representing information in compact form. Despite rapid
progress in mass-storage density, processor speeds, and digital
communication system performance, demand for data storage
capacity and data-transmission bandwidth continues to
outstrip the capabilities of available technologies. In a
distributed environment large image files remain a major
bottleneck within systems.
Image Compression is an important component of the
solutions available for creating image file sizes of manageable
and transmittable dimensions. Platform portability and
performance are important in the selection of the
compression/decompression technique to be employed.
1.2 Picture Quality Measures
Digital image is represented as matrix, where M denotes
number of columns and N number of rows.
and
denotes pixel values of original image before compression and
degraded image after compression.
Mean Square Error (MSE) =
Peak Signal to Noise Ratio (PSNR) =
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=
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
Normalized Cross-Correlation (NK) =
Average Difference (AD) =
diagnosis. Wavelet transforms present one such approach for
the purpose of compression. The same has been explored in
paper with respect to wide variety of medical images. In this
approach, the redundancy of the medical image and DWT
coefficients are reduced through thresholding and further
through Huffman encoding. This paper presents a lossy image
compression technique which works well over most of the
medical images
Structural Content (SC) =
\
III.NEURAL NETWORK
The term neural network was traditionally used to refer to a
networ or circuit of biological neurons. The modern usage of
the term often refers to artificial neural networks, which are
composed of artificial neurons or nodes.
Backpropagation
Maximum Difference (MD)
=
Picture Quality Scale (PQS) =
1.3. Waves and wavelets
A wave is an oscillating function of time or space and is
periodic. In contrast, wavelets are localized waves. They have
their energy concentrated in time or space and are suited to
analysis of transient signals.[2] While Fourier Transform and
STFT use waves to analyze signals, the Wavelet Transform
uses wavelets of finite energy[3]. The Discrete Wavelet
Transform (DWT), which is based on sub-band coding, is
found to yield a fast computation of Wavelet Transform. It is
easy to implement and reduces the computation time and
resources required. Sampled input image is decomposed into
various frequency sub-bands or sub-band signals
II,LITERATURE SURVEY
The goal of any supervised learning algorithm is to find a
function that best maps a set of inputs to its correct output. An
example would be a simple classification task, where the input
is an image of an animal, and the correct output would be the
name of the animal.
IV. METHODOLOGY USED
1.
V. K. Bairagi and A. M. Sapkal (2009) have proposed
medical images carry huge and vital information. It is
necessary to compress the medical images without losing its
vital-ness. Wavelet transform is preferable in the field of
compression because of much more information of image can
be retraced by it than any other transform. There are varieties
of wavelets available but always there is a question in
designers mind about which type of wavelet should I select for
my design? Here we have focused on the selection of wavelet
for compression of medical images. Various wavelets have
been used for compression and we have done quality analysis
on reconstructed image. Based on results that bi-orthogonal
4.4 wavelet, we are getting least MSE with highest PSNR
Ruchika, Mooninder Singh, Anant Raj Singh(2012) Medical
image compression is necessary for huge database storage in
Medical Centres and medical data transfer for the purpose of
V RESULTS AND DISCUSSIONS
MATLAB is used for implementation.The name MATLAB
stands for matrix laboratory. MATLAB is a high-performance
language for technical computing. It integrates computation,
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It is a supervised learning method, and is a generalization of
the delta rule. It requires a dataset of the desired output for
many inputs, making up the training set.[3] It is most useful
for feed-forward networks (networks that have no feedback,
or simply, that have no connections that loop).
Backpropagation requires that the activation function used by
the artificial neurons (or "nodes") be differentiable.
2.
3.
4.
5.
6.
7.
Selection of best wavelet for medical Radiographs.
 Compress input image with each wavelet
with fixed CR.
 Find the following parameters to make
comparison -MSE, PSNR, Correlation,
Average difference, Normalized absolute
error, Structural content.
Make database using selected wavelet.
Save each with its optimum compression ratio using
subjective and objective evaluation.
Train and testing of neural network.
Apply input image to neural network with unknown
CR.
Compress this image with CR defined by neural
network.
Comparison based upon following parameters
CR,PSNR,MSE
visualization, and programming in an easy-to-use environment
where problems and solutions are expressed in familiar
mathematical notation.
Firstly we have done selection of best wavelet for
Radiographs image.and the compressed Radiograph images.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
Then will test and train neural network.
Choice of wavelet
Table 1: Analysis of Radiograph image compression using various Wavelets
Wavelet
THR
CR
MSE
PSNR
MD
db3
1
53.2700
0.0721
137.2010
1.4642
Haar
coif2
1
0.6
25.5982
47.9403
0.0647
0.0263
138.2833
147.3022
1.5000
0.8857
bio1.3
bio2.6
bio2.8
bio3.1
bior3.3
bior3.5
bior3.7
bior4.4
bior6.8
rbio1.1
rbio2.2
rbio2.4
coif1
sym2
1
0.9
0.7
0.8
1
0.6
0.9
1
0.5
1
0.7
0.9
0.8
1
29.3390
51.4927
47.0160
53.3054
59.7664
50.9821
60.3195
57.5266
48.6060
25.5982
31.5582
45.2295
45.0037
48.5041
0.0649
0.0830
0.0506
0.1105
0.1242
0.0507
0.0989
0.0811
0.0189
0.0647
0.0239
0.0498
0.0442
0.0727
138.2479
135.7911
140.7431
132.9291
131.7653
140.7294
134.0403
135.9313
150.5766
138.2633
148.2233
140.9090
142.0940
137.1212
1.5625
1.6842
1.2387
1.8906
1.9113
1.3063
1.7473
1.5966
0.7459
1.5000
1.0391
1.4490
1.2330
1.6415
It is clearly seen that, for bi-orthogonal 6.8 wavelet, we are
getting least MSE and highest PSNR of 150.5766 db with
fixed CR. From the above observation table it is more cleared
SC
NAE
CC
1
0.0022
1
1
0.0016
0.0013
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.0019
0.0023
0.0018
0.0027
0.0029
0.0018
0.0026
0.0023
0.0011
0.0010
0.0011
0.0018
0.0017
0.0022
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
with mathematical formulae support, that bi-orthogonal type
6.8 wavelet is best suitable for Radiograph image.
Figures1: Shows Compressed image and their Comparison using biorthogonal and Haar wavelets
ORIGINAL IMAGE
USING BIORTHO 6.8 WAVELETS
(THR=0.5)
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(CR= 49)
ORIGINAL IMAGE
(THR=0.5)
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USING HAAR WAVELETS
(CR= 10)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
Figure 2: Training and Testing of Neural Network
Figure 2.1 Performance Graph
Perfomance graph shows that Final Mean square error is small.Test set error and Validation set error have similar Characterst ics.
No significant overfitting occours by iteration8.
Training state shows the progress of other variables like Gradient and number of validation checks.
Figure 2.2 Training State
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
Figure2.3 Regression Graph
VI.CONCLUSION
with mathematical formulae support, that biorthogonal type
6.8 wavelet is best suitable for Radiograph image.
IIt is clearly seen that, for bi-orthogonal 6.8 wavelet, we are
getting least MSE and highest PSNR of 150.5766 db with
fixed CR. From the above observation table it is more cleared
.
[3] Krishan kumar,Basant kumar,Rachna shah” Analysis of Efficient
VII.FUTURE SCOPE
Wavelet Based Volumetric Image Compression” International Journal of
Image Processing (IJIP), Volume (6) : Issue (2) : 2012
In future we can apply JPEG (Joint Photographic Expert
Group) compression method instead of threshold for finding
better compression. Another scope is to optimized the
compression method by using AI (Artificial Intelligence)
algorithm. We can use this method and make comparison of
biortho 6.8 with other Wavelets
[4] Adnan Khashman and Kamil Dimililer, “Comparison Criteria for
Optimum Image Compression.” Proceeding in EUROCON 2005 Serbia &
Montenegro, Belgrade, Nov 22-24, 2005.
[5] V. K. Bhairagi and A. M. Sapkal, “Selection of Wavelets for Medical
Image Compression.” Proceeding in 2009 International Conference on
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