chapter 7 conclusion and scope for future...
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CHAPTER 7
CONCLUSION AND SCOPE FOR FUTURE WORK
7.1 CONCLUSION
Efficient image compression techniques are becoming very vital in
areas like pattern recognition, image processing, system modeling, data
mining, etc. Compression techniques have become the most
concentrated area in the field of computer. Image compression is a
technique of efficiently coding digital image to reduce the number of bits
required in representing an image. The present research work proposes
three novel techniques using vector quantization for effective image
compression. Code book generation using vector quantization is the
principal step in this research work. The present research work uses
effective clustering technique for the code book generation. Effective
clustering techniques such as Modified K-Means, Modified Fuzzy
Possibilistic C-Means with Repulsion, Modified Fuzzy Possibilistic C-
Means with repulsion and Weighted Mahalanobis Distance are used in
this research for better compression results.
The performance of the proposed approaches is evaluated on the
basis of parametric standards like SSE, Entropy, Execution Time and
PSNR value. The performance is compared to the standard approaches
like K-Means, LBG and MFPCM. It is clearly observed from the
experimental results that the proposed approaches outperform the
standard approaches like K-Means, MFPCM and LBG. The performance
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156
is evaluated on three standard images like Lena, Cameraman and
Peppers.
Among the proposed approaches, the proposed Modified Fuzzy
Possibilistic C-Means with repulsion and Weighted Mahalanobis
Distance approach used for code generation has the least SSE when
compared to the other proposed approaches. Similarly, the entropy
value, Execution time and Coding of VQ indices are also very much less
when compared to the other approaches reviewed in the literature.
PSNR value of the proposed Modified Fuzzy Possibilistic C-
Means with repulsion and Weighted Mahalanobis Distance approach is
very high when compared to the other proposed approaches.
Thus, the proposed image compression technique which uses
Modified Fuzzy Possibilistic C-Means with repulsion and Weighted
Mahalanobis Distance for code book generation outperforms the other
proposed approaches in terms of all the parameters taken into
consideration.
7.2 SCOPE FOR FUTURE WORK
The present research mainly focused on the effective image
compression techniques using vector quantization approaches. Code
book generation is the main technique that has been taken up for
research in this thesis. The present research has used three effective
code book generation approaches for efficient image compression
techniques. Code book generation is based on the clustering
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157
approaches such as Modified K-Means, Modified Fuzzy Possibilistic C-
Means with repulsion, Modified Fuzzy Possibilistic C-Means with
repulsion and Weighted Mahalanobis Distance. It is observed from the
experimental results that the proposed approach provides better results
when compared to the standard code book generation techniques.
The future enhancement of this research work would be to
increase the PSNR value with less computation time. Some of the future
extensions of this research are listed below:
Recent clustering techniques based on the Swarm Intelligence (AI)
may be used for code book generation which may increase the
overall performance of the system.
The techniques based on the evolutionary algorithms (Ants, Bees
etc.,) which may be used to provide better optimized results, which
make use of genetic algorithm.
Effective Neuro-fuzzy techniques can be incorporated with the
vector quantization technique for better overall performance.
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158
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LIST OF PUBLICATIONS
CONFERENCES
1. Distributed Data Warehouse Construction and Accessing
Tool, UGC Sponsored National Conference on Computer
Science and Informatics, St.Joseph College, Trichy, 18th & 19th
February 2005.
2. Image Mining Using Integrated Image Features, UGC
Sponsored National Conference on Data Mining and its
Applications, Gobi Arts & Science College, Gobichettipalayam,
9th and 10th March 2007.
3. Multicast Group Based Computation Time Reduction Scheme
for Grid Environment, UGC Sponsored National Conference on
Web Services, PSG College of Arts & Science College,
Coimbatore, 20th and 21st March 2007.
4. Adaptive Web Search and Navigation Using PWNE , UGC
Sponsored National Conference on Data Warehousing and
Data Mining, Gobi Arts & Science College, Gobichettipalayam,
20th and 21th March 2011.
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177
JOURNALS
1. Image compression using vector quantization – A survey
approach, CiiT International Journal of Digital Image Processing,
Vol. 1.No. 8, Nov 2009.
2. An Efficient Vector Quantization method for Image Compression
with codebook generation using modified K-Means, International
Journal of Computer Science and Information Security, Vol. 8,
No. 8, Nov 2010.
3. An Enhanced Vector Quantization Method for Image compression
with modified Fuzzy possibilistic C-Means using Repulsion,
International Journal of Computer Applications, Vol. 21, No. 5,
May 2011.
4. A Novel Vector Quantization Technique for Image Compression
with Enhanced Fuzzy Possibilistic C-Means using Standard
Mahalanobis Distance International Journal of Electrical,
Electronics and Computer Systems, Vol.3,Issue 2, Aug 2011.
5. Vector Quantization for Image Compression using Repulsion
based FPCM, International Journal of Computer Science and
Information Technologies, Vol.2 (5), Sep 2011.
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