AUTHORS: Mourad Rahali, Habiba Loukil, Mohamed Salim Bouhlel
Download as PDF
ABSTRACT: Since digital images require a large space on the storage devices and the network bandwidth, many compression methods have been used to solve this problem. Actually, these methods have, more or less, good results in terms of compression ratio and the quality of the reconstructed images. There are two main types of compression: the lossless compression which is based on the scalar quantization and the lossy compression which rests on the vector quantization. Among the vector quantization algorithms, we can cite the Kohonen's network. To improve the compression result, we add a pre-processing phase. This phase is performed on the image before applying the Kohonen's network of compression. Such a phase is the wavelet transform. Indeed, this paper is meant to study and model an approach to image compression by using the wavelet transform and Kohonen's network. The compression settings for the approach to the model are based on the quality metrics rwPSNR and MSSIM.
KEYWORDS: Image compression, Kohonen's networks, wavelet transform, learning algorithm, rwPSNR, MSSIM
REFERENCES:
[1]. D. Koff and H. Shulman, “An Overview of Digital Compression of Medical Images: Can We Use Lossy Image Compression in Radiology?”, Canadian Association of Radiologists Journal,Vol 57, N°4, October 2006.
[2]. N. Zhang and X. Wu “Lossless compression of color mosaic images”, Image Processing, IEEE Transactions on Vol 15, Issue 6, pages 1379 – 1388, June 2006.
[3]. J Jiang, “Neural network technology for image compression”, International Broadcasting Convention, 14-1, Conference Publication N° 413, IEEE, September 1995.
[4]. T. Kohonen, “The self-organizing map”, proceeding of the IEEE, Vol 78, N° 9, September 1990.
[5]. J. Pujar and L. Kadlaskar, “ A New Lossless Method Of Image Compression And Decompression Using Huffman Coding Techniques”, Journal of Theoretical & Applied Information Technology, Vol. 15 Issue 1/2, p18- 23, 2010.
[6]. J. Ribés, B. Simon and B. Macq “Combined Kohonen neural networks and discrete cosine transform method for iterated transformation theory”, Vol 16, Issue 7, Pages 643-656, April 2001.
[7]. P. Raghuwanshi and A. Jain “A Review of Image Compression based on Wavelet Transform Function and Structure optimization Technique”, International Journal Computer Technology and Applications, Vol 4, pages 527- 532, June 2013.
[8]. G. Boopathi, “An Image Compression Approach using Wavelet Transform and Modified Self Organizing Map”, International Journal of Computer Science Issues, Vol. 8, Issue 5, N° 2, September 2011.
[9]. A. S. Lewis, G. Knowles, “Image compression using the 2-D wavelet transform”, IEEE Transactions on Image Processing, Vol 1, Issue 2, pp 244-250, April 1992
[10]. H. Loukil, M. H. Kacem and M. S. Bouhlel, “A New Image Quality Metric Using System Visual Human Characteristics”, International Journal of Computer Applications, Vol 60, N° 6, December 2012.
[11]. Alan C. Brooks, Xiaonan Zhao and Thrasyvoulos N. Pappas, “Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions”, IEEE Transactions On Image Processing, Vol. 17, N° 8, August 2008.
[12]. M. K. Mathur, S. Loonker, D. Saxena, “Lossless Huffman Coding Technique for image Compression And Reconstruction Using binary Trees” international journal of computer technology and applications,Vol 3, N°1, pp76- 79,Feb 2012.
[13]. S. A. Alshehri, “Neural Network Technique for Image Compression” IET Image Processing, pp. 1–5, September 2015.
[14]. H. Wang, L. Lu, D. Que, X. Luo, “Image Compression Based Om Wavelet Transform And Vector Quantization”, International Conference on Machine Learning and Cybernetics, Beijing, 45 November 2002.
