On the best evolutionary wavelet based filter to compress a specific signal

Authors:
Oscar Herrera Alcántara
Affiliations:
Departamento de Sistemas, Universidad Autónoma Metropolitana Azcapotzalco, Reynosa, Tamaulipas, México, D.F.
Venue:
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Year:
2010

Citing 3
Cited 1

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets

Ten lectures on wavelets
Algorithms for designing wavelets to match a specified signal

IEEE Transactions on Signal Processing

Optimization of parameterized compactly supported orthogonal wavelets for data compression

MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents an application of a genetic algorithm in the design of digital filters used to implement the discrete wavelet transform. The best compression of a transformed signal is achieved when its power is described by the smallest number of transformation coefficients. The individuals of the genetic algorithm aim to reach this target, at the same time that they reduce the error of the reconstructed signal. In the experiments we worked with grayscale images, and we compared the performance of evolutionary and Daubechies filters. Experimental results show the feasibility and convenience of finding custom wavelets for each image, and support the idea that there is a suitable wavelet to compress any given signal.