Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

Se presenta un metodo para compresion de imagenes sin perdidas, usando la transformada wavelet lifting con ajuste automatico de coeficientes de filtros wavelets para una mayor compresion sin perdidas. La propuesta se basa en reconocimiento de patrones utilizando clasificador 1-NN. Utilizando el reconocimiento de patrones se optimizan los coeficientes de los filtros lifting de manera global para cada imagen. La tecnica propuesta fue aplicada para la compresion de imagenes de prueba y comparada con los filtros wavelets estandares CDF (2,2) y CDF (4,4), obteniendo resultados mejores en relacion a la entropia obtenida para cada imagenes, asi, como para el promedio general

[1]  Shiv Dutt Joshi,et al.  A new approach for estimation of statistically matched wavelet , 2005, IEEE Transactions on Signal Processing.

[2]  D. Taskovski,et al.  Adaptive lifting integer wavelet transform for lossless image compression , 2008, 2008 15th International Conference on Systems, Signals and Image Processing.

[3]  Günther Palm,et al.  A Study of the Robustness of KNN Classifiers Trained Using Soft Labels , 2006, ANNPR.

[4]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[5]  Seiji Hotta,et al.  Pattern recognition using average patterns of categorical k-nearest neighbors , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  I. Daubechies,et al.  Wavelet Transforms That Map Integers to Integers , 1998 .

[7]  Deepen Sinha,et al.  On the optimal choice of a wavelet for signal representation , 1992, IEEE Trans. Inf. Theory.

[8]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[11]  Amel Benazza-Benyahia,et al.  Adaptive lifting scheme with sparse criteria for image coding , 2012, EURASIP J. Adv. Signal Process..

[12]  Henning Thielemann,et al.  Optimally matched wavelets , 2004 .

[13]  M. Vetterli Multi-dimensional sub-band coding: Some theory and algorithms , 1984 .

[14]  A. Aldroubi,et al.  Families of multiresolution and wavelet spaces with optimal properties , 1993 .

[15]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[16]  Michael G. Strintzis,et al.  Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding , 2001, IEEE Trans. Image Process..

[17]  Amit Dhurandhar,et al.  Probabilistic characterization of nearest neighbor classifier , 2012, International Journal of Machine Learning and Cybernetics.

[18]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[19]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[20]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[21]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[22]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[23]  Wim Sweldens,et al.  Lifting scheme: a new philosophy in biorthogonal wavelet constructions , 1995, Optics + Photonics.

[24]  N. Peng,et al.  A SVM-kNN method for quasar-star classification , 2013, Science China Physics Mechanics and Astronomy.

[25]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[26]  Guizhong Liu,et al.  Optimization of integer wavelet transforms based on difference correlation structures , 2005, IEEE Trans. Image Process..

[27]  Raghuveer M. Rao,et al.  Algorithms for designing wavelets to match a specified signal , 2000, IEEE Trans. Signal Process..

[28]  Yang Song,et al.  IKNN: Informative K-Nearest Neighbor Pattern Classification , 2007, PKDD.