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Union is Strength in Lossy Image Compression
Authors: Mario Mastriani
Abstract:In this work, we present a comparison between different techniques of image compression. First, the image is divided in blocks which are organized according to a certain scan. Later, several compression techniques are applied, combined or alone. Such techniques are: wavelets (Haar's basis), Karhunen-Loève Transform, etc. Simulations show that the combined versions are the best, with minor Mean Squared Error (MSE), and higher Peak Signal to Noise Ratio (PSNR) and better image quality, even in the presence of noise.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081733Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1391
 J.-L. Starck, and P. Querre, "Multispectral Data Restoration by the Wavelet-Karhunen-Loève Transform," Preprint submitted to Elsevier Preprint, 2000, pp. 1-29.
 D. L. Donoho, and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Journal of the American Statistical Assoc., vol. 90, no. 432, pp. 1200-1224., 1995.
 D. L. Donoho, and I. M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, 81, 425-455, 1994.
 I. Daubechies. Ten Lectures on Wavelets, SIAM, Philadelphia, PA. 1992.
 I. Daubechies, "Different Perspectives on Wavelets," in Proceedings of Symposia in Applied Mathematics, vol. 47, American Mathematical Society, USA, 1993.
 S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 674-693, July 1989.
 S. G. Mallat, "Multiresolution approximations and wavelet orthonormal bases of L2 (R)," Transactions of the American Mathematical Society, 315(1), pp.69-87, 1989a.
 S. G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Processing, vol. 9, pp. 1532-1546, Sept. 2000.
 M. Misiti, Y. Misiti, G. Oppenheim, and J.M. Poggi. (2001, June). Wavelet Toolbox, for use with MATLAB®, User-s guide, version 2.1.
 C.S. Burrus, R.A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, Prentice Hall, New Jersey, 1998.
 B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A. K. Peter Wellesley, Massachusetts, 1996.
 A. Grossman and J. Morlet, "Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape," SIAM J. App Math, 15: pp.723-736, 1984.
 C. Valens. (2004). A really friendly guide to wavelets.
 G. Kaiser, A Friendly Guide To Wavelets, Boston: Birkhauser, 1994.
 J.S. Walker, A Primer on Wavelets and their Scientific Applications, Chapman & Hall/CRC, New York, 1999.
 E. J. Stollnitz, T. D. DeRose, and D. H. Salesin, Wavelets for Computer Graphics: Theory and Applications, Morgan Kaufmann Publishers, San Francisco, 1996.
 J. Shen and G. Strang, "The zeros of the Daubechies polynomials," in Proc. American Mathematical Society, 1996.
 H.Y. Gao, and A.G. Bruce, "WaveShrink with firm shrinkage," Statistica Sinica, 7, 855-874, 1997.
 V.S. Shingate, et al, "Still image compression using Embedded Zerotree Wavelet Encoding," In International Conference on Cognitive Systems New Delhi, December 14-15, pp.1-9, 2004.
 C. Valens, EZW encoding, 2004.
 Yuan-Yuan HU, et al, "Embedded Wavelet Image Compression Algorithm Based on Full Zerotree," In IJCSES International Journal of Computer Sciences and Engineering Systems, Vol.1, No.2, pp. 131-138, April 2007.
 C.Wang, and K-L. Ma, "A Statistical Approach to Volume Data Quality Assessment," IEEE Transactions on Visualization and Computer Graphics, Vol.14, No. 3, May/June, 2008, pp. 590-602.
 R. Muller, A study of image compression techniques, with specific focus on weighted finite automata, Thesis for Degree Master of Science at the University of Stellenbosch, December 2005.
 S.S. Polisetty, Hardware acceleration of the embedded zerotree wavelet algorithm, Thesis for Master of Science Degree at the University of Tennessee, Knoxville, December 2004.
 V. Mahomed, and S.H. Mneney, "Wavelet based compression: the new still image compression technique," In Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, 23-25 November 2005, Langebaan, South Africa, pp.67-72.
 H.A. Kim Taavo, Scalable video using wavelets, Master of Science Programme, Lulea University of Technology, May 14, 2002.
 D. Zhang, and S. Chen, "Fast image compression using matrix K-L transform," Neurocomputing, Volume 68, pp.258-266, October 2005.
 R.C. Gonzalez, R.E. Woods, Digital Image Processing, 2nd Edition, Prentice- Hall, Jan. 2002, pp.675-683.
 -, The transform and data compression handbook, Edited by K.R. Rao, and P.C. Yip, CRC Press Inc., Boca Raton, FL, USA, 2001.
 B.R. Epstein, et al, "Multispectral KLT-wavelet data compression for landsat thematic mapper images," In Data Compression Conference, pp. 200-208, Snowbird, UT, March 1992.
 K. Konstantinides, et al, "Noise using block-based singular value decomposition," IEEE Transactions on Image Processing, 6(3), pp.479- 483, 1997.
 J. Lee, "Optimized quadtree for Karhunen-Loève Transform in multispectral image coding," IEEE Transactions on Image Processing, 8(4), pp.453-461, 1999.
 J.A. Saghri, et al, "Practical Transform coding of multispectral imagery," IEEE Signal Processing Magazine, 12, pp.32-43, 1995.
 T-S. Kim, et al, "Multispectral image data compression using classified prediction and KLT in wavelet transform domain," IEICE Transactions on Fundam Electron Commun Comput Sci, Vol. E86-A; No.6, pp.1492- 1497, 2003.
 J.L. Semmlow, Biosignal and biomedical image processing: MATLABBased applications, Marcel Dekker, Inc., New York, 2004.
 S. Borman, and R. Stevenson, "Image sequence processing," Department, Ed. Marcel Dekker, New York, 2003. pp.840-879.
 M. Wien, Variable Block-Size Transforms for Hybrid Video Coding, Degree Thesis, Institut f├╝r Nachrichtentechnik der Rheinisch- Westf├ñlischen Technischen Hchschule Aachen, February 2004.
 E. Christophe, et al, "Hyperspectral image compression: adapting SPIHT and EZW to anisotopic 3D wavelet coding," submitted to IEEE Transactions on Image processing, pp.1-13, 2006.
 L.S. Rodr├¡guez del R├¡o, "Fast piecewise linear predictors for lossless compression of hyperspectral imagery," Thesis for Degree in Master of Science in Electrical Engineering, University of Puerto Rico, Mayaguez Campus, 2003.
 Hyperspectral Data Compression, Edited by Giovanni Motta, Francesco Rizzo and James A. Storer, Chapter 3, Springer, New York, 2006.
 B. Arnold, An Investigation into using Singular Value Decomposition as a method of Image Compression, University of Canterbury Department of Mathematics and Statistics, September 2000.
 S. Tjoa, et al, "Transform coder classification for digital image forensics".
 M. Mastriani, and A. Giraldez, "Smoothing of coefficients in wavelet domain for speckle reduction in Synthetic Aperture Radar images," ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 6, pp. 1-8, 2005.
 M. Mastriani, and A. Giraldez, "Despeckling of SAR images in wavelet domain," GIS Development Magazine, Sept. 2005, Vol. 9, Issue 9, pp.38-40.
 M. Mastriani, and A. Giraldez, "Microarrays denoising via smoothing of coefficients in wavelet domain," WSEAS Transactions on Biology and Biomedicine, 2005.
 M. Mastriani, and A. Giraldez, "Fuzzy thresholding in wavelet domain for speckle reduction in Synthetic Aperture Radar images," ICGST International on Journal of Artificial Intelligence and Machine Learning, Volume 5, 2005.
 M. Mastriani, "Denoising based on wavelets and deblurring via selforganizing map for Synthetic Aperture Radar images," ICGST International on Journal of Artificial Intelligence and Machine Learning, Volume 5, 2005.
 M. Mastriani, "Systholic Boolean Orthonormalizer Network in Wavelet Domain for Microarray Denoising," ICGST International Journal on Bioinformatics and Medical Engineering, Volume 5, 2005.
 M. Mastriani, "Denoising based on wavelets and deblurring via selforganizing map for Synthetic Aperture Radar images," International Journal of Signal Processing, Volume 2, Number 4, pp.226-235, 2005.
 M. Mastriani, "Systholic Boolean Orthonormalizer Network in Wavelet Domain for Microarray Denoising," International Journal of Signal Processing, Volume 2, Number 4, pp.273-284, 2005.
 M. Mastriani, and A. Giraldez, "Microarrays denoising via smoothing of coefficients in wavelet domain," International Journal of Biomedical Sciences, Volume 1, Number 1, pp.7-14, 2006.
 M. Mastriani, and A. Giraldez, "Kalman- Shrinkage for Wavelet-Based Despeckling of SAR Images," International Journal of Intelligent Systems and Technologies, Volume 1, Number 3, pp.190-196, 2006.
 M. Mastriani, and A. Giraldez, "Neural Shrinkage for Wavelet-Based SAR Despeckling," International Journal of Systems and Technologies, Volume 1, Number 3, pp.211-222, 2006.
 M. Mastriani, "Fuzzy Thresholding in Wavelet Domain for Speckle Reduction in Synthetic Aperture Radar Images," International Journal of Systems and Technologies, Volume 1, Number 3, pp.252-265, 2006.
 M. Mastriani, "New Wavelet-Based Superresolution Algorithm for Speckle Reduction in SAR Images ," International Journal of Computer Science, Volume 1, Number 4, pp.291-298, 2006.
 M. Mastriani, "Denoising and compression in wavelet domain via projection onto approximation coefficients," International Journal of Signal Processing, Volume 5, Number 1, pp.20-30, 2008.
 H. Samet, The Design and Analysis of Spatial Data Structures, Addison- Wesley, 1990.
 H. Samet, Applications of Spatial Data Structures - Computer Graphics, Image Processing and GIS, Addison-Wesley, 1990.
 A.K. Jain, Fundamentals of Digital Image Processing, Englewood Cliffs, New Jersey, 1989.
 MATLAB® R2008a (Mathworks, Natick, MA).
 A. Sharma, and K.K. Paliwal, "Fast principal component analysis using fixed-point algorithm", Pattern Recognition Letters, Vol.28, pp.1151- 1155, 2007.
 E. Oja, "A Simplified Neuron Model as a Principal Component Analyzer", Journal of Mathematical Biology, Vol.15, pp.267-273, 1982.
 T.D. Sanger, "Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Networks," Vol.2, pp.459-473, 1989.