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Kumar, B. Vinoth
- Single Versus Multiple Trial Vectors in Classical Differential Evolution for Optimizing the Quantization Table in JPEG Baseline Algorithm
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Authors
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1 Department of Computer Science and Engineering, PSG College of Technology, IN
1 Department of Computer Science and Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 4 (2017), Pagination: 1510-1516Abstract
Quantization Table is responsible for compression / quality trade-off in baseline Joint Photographic Experts Group (JPEG) algorithm and therefore it is viewed as an optimization problem. In the literature, it has been found that Classical Differential Evolution (CDE) is a promising algorithm to generate the optimal quantization table. However, the searching capability of CDE could be limited due to generation of single trial vector in an iteration which in turn reduces the convergence speed. This paper studies the performance of CDE by employing multiple trial vectors in a single iteration. An extensive performance analysis has been made between CDE and CDE with multiple trial vectors in terms of Optimization process, accuracy, convergence speed and reliability. The analysis report reveals that CDE with multiple trial vectors improves the convergence speed of CDE and the same is confirmed using a statistical hypothesis test (t-test).Keywords
Meta-Heuristic Search, Differential Evolution, Trial Vectors, Image Compression, JPEG,, Quantization Table, Optimization, Statistical Hypothesis Test And t-Test.
References
- W3Tech, “Usage of JPEG for websites”, Available at:www.w3techs.com/technologies/details/im-jpeg/all/all.Accessed on 2017.
- Gregory K. Wallace, “The JPEG still Picture Compression Standard”, Communications of the ACM-Special Issue on Digital Multimedia Systems, Vol. 34, No. 4, pp.30-44, 1991.
- S. Viswajaa, B. Vinoth Kumar and G.R. Karpagam, “A Survey on Nature Inspired Meta-Heuristics Algorithms in Optimizing the Quantization Table for the JPEG Baseline Algorithm”, International Advanced Research Journal in Science, Engineering and Technology, Vol. 2, No. 4.pp.114-123, 2015.
- S.P. Naresh, B. Vinoth Kumar and G.R. Karpagam, “A Literature Review on Quantization Table Design for the JPEG Baseline Algorithm”, International Journal of Engineering and Computer Science, Vol. 4, No. 10, pp.14686-14691, 2015.
- B.G. Sherlock, A. Nagpal and D.M. Monro, “A Model for JPEG Quantization”, Proceedings of International Symposium on Speech, Image Processing and Neural Networks, pp. 176-179, 1994.
- Yung-Gi Wu, “GA-based DCT Quantization Table Design Procedure for Medical Images”, IEE Proceedings-Vision, Image and Signal Processing, Vol. 151, No. 5, pp. 353-359, 2004.
- M.R. Boyandi, E. Dehghani and M.E. Moghaddam, “A NonUniform Image Compression using Genetic Algorithm”, Proceedings of 15th International Conference on Systems, Signals and Image Processing, pp. 315-318, 2008.
- Shuwang Chen, Tao An and Litao Hao, “Discrete Cosine Transform Image Compression Based on Genetic Algorithm”, International Conference on Information Engineering and Computer Science, pp. 1-3, 2009.
- Beatrice Lazzerini, Francesco Marcelloni and Massimo Vecchio, “A Multi-Objective Evolutionary Approach to Image Quality/Compression Trade-Off in JPEG Baseline Algorithm”, Applied Soft Computing, Vol. 10, No. 1, pp. 548-561, 2010.
- B. Vinoth Kumar, G.R. Karpagam and N. Vijaya Rekha, “Performance Analysis of Deterministic Centroid Initialization Method for Partitional Algorithms in Image Block Clustering”, Indian Journal of Science and Technology, Vol. 8, No. 7, pp. 63-73, 2015.
- B Vinoth Kumar and Karpagam Manavalan, “Knowledge based Genetic Algorithm Approach to Quantization Table Generation for the JPEG Baseline Algorithm”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 24. No. 3, pp. 1615-1635, 2016.
- B Vinoth Kumar and Karpagam Manavalan, “A Problem Approximation Surrogate Model (PASM) for Fitness Approximation in Optimizing the Quantization Table for the JPEG Baseline Algorithm”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 24, No. 6, pp.4623-4636, 2016.
- S.M. Guo, J.S.H Tsai, W.H. Chang, B.H. Wu and S.J. Guo, “Chaos Evolutionary Programming based JPEG Quantization Table Generation Scheme”, Proceedings of 5th International Conference on Informatics and Systems, pp. 1-7, 2007.
- Huizhu Ma and Qiuju Zhang, “Research on Cultural-based Multi-Objective Particle Swarm Optimization in Image Compression Quality Assessment”, Optik-International Journal for Light and Electron Optics, Vol. 124, No. 10, pp. 957-961, 2012.
- Milan Tuba and Nebojsa Bacanin, “JPEG Quantization Tables Selection by the Firefly Algorithm”, Proceedings of International Conference on Multimedia Computing and Systems, pp. 1-6, 2014.
- B. Vinoth Kumar and M. Karpagam, “Differential Evolution versus Genetic Algorithm in Optimising the Quantisation Table for JPEG Baseline Algorithm”, International Journal of Advanced Intelligence Paradigms, Vol. 7, No. 2, pp. 111135, 2015.
- B. Vinoth Kumar and Karpagam Manavalan, “Knowledge based Differential Evolution Approach to Quantization Table Generation for the JPEG Baseline Algorithm”, International Journal of Advanced Intelligence Paradigms, Vol. 8, No. 1, pp. 20-41, 2016.
- B. Vinoth Kumar, G.R. Karpagam and S.P. Naresh, “Generation of JPEG Quantization Table using Real Coded Quantum Genetic Algorithm”, Proceedings of IEEE International Conference on Communication and Signal Processing, pp. 1705-1709, 2016.
- Rainer Storn and Kenneth Price, “Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Vol. 11, No. 4, pp. 341-359, 1997.
- Swagatam Das and Ponnuthurai Nagaratnam Suganthan, “Differential Evolution: A survey of the State-of-the-Art”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 4-31, 2011.
- Bernabe Dorronsoro and Pascal Bouvry, “Improving Classical and Decentralized Differential Evolution with new Mutation Operator and Population Topologies”, IEEE Transactions on Evolutionary Computing, Vol. 15, No. 1, pp. 67-98, 2011.
- Ivan Zelinka, Vaclav Snasael and Ajith Abraham, “Handbook of Optimization: From Classical to Modern Approach”, Springer, 2012.
- Yong Wang, Zixing Cai and Qingfu Zhang, “Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 55-66, 2011.
- Z. Hui-Fang, H. Wei and W. Jin-Song, “Clustering-Based Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters”, Proceedings of Joint International Conference on Service Science, Management and Engineering. pp. 1-6, 2016.
- Kenneth Price, Rainer M. Storn and Jouni A Lampinen, “Differential Evolution: A Practical Approach to Global Optimization”, Springer, 2006.