The week's pick
Reseach Article
Image Compression: An approach using Wavelet Transform and Modified FCM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 28 - Number 2 |
| Year of Publication: 2011 |
| Authors: G Boopathi, Dr.S.Arockiasamy |
10.5120/3363-4643
|
G Boopathi, Dr.S.Arockiasamy . Image Compression: An approach using Wavelet Transform and Modified FCM. International Journal of Computer Applications. 28, 2 ( August 2011), 7-12. DOI=10.5120/3363-4643
Abstract
In recent past, vector quantization has been observed as an efficient technique for image compression. In general, image compression reduces the number bits required to represent an image. The main significance of image compression is that the quality of the image is preserved. This in turn increases the storage space and thereby the volume of the data that can be stored. Image compression is the application of data compression technique on digital images. Wavelet Transform based image compression remain the most common among diverse techniques proposed earlier. Wavelet-based image compression provides considerable improvements in picture quality at higher compression ratios. A moment ago Artificial Neural Network has attained popularity in the field of image compression. This paper proposes a technique for image compression using modified Fuzzy C-Means (FCM) algorithm based vector quantization (VQ). The VQ codebook is generated by a modified FCM algorithm. The principal shortcoming of standard FCM algorithm is that the objective function does not think about the spatial dependence therefore it deal with image as the same as separate points. This proposed paper modifies the standard FCM algorithm that join together both the local spatial context and the non-local information into the standard FCM cluster algorithm using a novel dissimilarity index in place of the usual distance metric. Experiments are carried out in order to estimate the performance of the proposed modified FCM algorithm in image compression using standard image set. The results exposed the performance of our approach in perspective with other conventional image compression techniques.
References
- M. J. Nadenau, J. Reichel, and M. Kunt, “Wavelet Based Color Image Compression: Exploiting the Contrast Sensitivity Function,” IEEE Transactions Image Processing, Vol. 12, no.1, Pp. 58-70, 2003.
- K. H. Talukder, and K. Harada, “Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image,” IAENG International Journal of Applied Mathematics, 2007.
- M. Egmont-Petersen, D. de. Ridder, and Handels, “Image Processing with Neural Networks – a review,” Elsevier, Journal on Pattern Recognition, Vol. 35, no. 10, Pp. 2279-2301, 2002.
- S. Osowski, R. Waszczuk, and P. Bojarczak, “Image compression using feed forward neural networks- Hierarchical approach,” Lecture Notes in Computer Science, Book Chapter, Springer-Verlag, Vol. 3497, Pp. 1009-1015, 2006.
- M. Liying, and K. Khashayar, “Adaptive Constructive Neural Networks Using Hermite Polynomials for Image Compression,” Lecture Notes in Computer Science, Springer-Verlag, Vol. 3497, Pp. 713-722, 2005.
- B. Karlik, “Medical Image Compression by Using Vector Quantization Neural Network,” ACAD Sciences press in Computer Science, Vol. 16, no. 4, Pp. 341-348, 2006.
- Christopher J. C. Burges, Henrique S. Malvar, and Patrice Y. Simard, “Improving Wavelet Image Compression with Neural Networks,” MSR-TR-2001-47, Pp. 1-18, August 2001.
- Adnan Khashman, and Kamil Dimililer, “Image Compression using Neural Networks and Haar Wavelet,” WSEAS Transactions on Image Processing, Vol. 4, no. 5, Pp. 330-339, 2008.
- Wilford Gillespie, “Still Image Compression using Neural Networks,” 2005.
- Y. H. Dandawate, T. R. Jadhav, A. V. Chitre, and M. A. Joshi, “Neuro-Wavelet based vector quantizer design for image compression,” Indian Journal of Science and Technology, Vol. 2, no. 10, Pp. 56-61, 2009.
- Ashutosh Dwivedi, N. Subhash Chandra Bose, Ashiwani Kumar, Prabhanjan Kandula, Deepak Mishra, and Prem K Kalra, “A Novel Hybrid Image Compression Technique: Wavelet-MFOCPN,” In Proceedings of ASID’06, Pp. 492-495, 2006.
- C. Ben Amar, and O. Jemai, “Wavelet Networks Approach for Image Compression,” ICGST, 2003.
- V. Singh, N. Rajpal, and K. S. Murthy, “Neuro-Wavelet Based Approach for Image Compression,” Computer Graphics, Imaging and Visualization, CGIV apos’07, Pp. 280-286, 2007.
- Jung-Hua Wang, and Ker-Jiang Gou, “Image compression using wavelet transform and self-development neural network,” IEEE International Conference on Systems, Man, and Cybernetics, Vol. 4, Pp. 4104-4108, 1998.
- Kussay Nugamesh Mutter, Zubir Mat Jafri, and Azlan Bin Abdul Aziz, “Hybrid Hopfield Neural Network, Discrete Wavelet Transform and Huffman Coding for Image Recognition,” IJCSNS International Journal of Computer Science and Network Security, Vol. 9, no. 6, Pp. 73-78, 2009.
- J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function algorithms,” Plenum Press, New York, 1981.
- A. Buades, B. Coll, and J. -M. Morel, “A non-local algorithm for image denoising,” In CVPR, Vol. 2, Pp. 60-65, 2005.
- A. Buades, B. Coll, J. -M. Morel, “On image denoising methods,” Technical Report 2004-15, CMLA, 2004.
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