Improve maximum likelihood estimation for subband GGD parameters

Authors:
Minghong Pi
Affiliations:
Dept. of Mathematics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China and Department of Computing Science, University of Alberta, Edmonton AB, Canada T6G 2E8
Venue:
Pattern Recognition Letters
Year:
2006

Citing 12
Cited 3

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pyramid-based texture analysis/synthesis

SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Texture Classification by Wavelet Packet Signatures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling

Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels

IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain

IEEE Transactions on Signal Processing
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors

IEEE Transactions on Information Theory
Statistical texture characterization from discrete wavelet representations

IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform

IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames

IEEE Transactions on Image Processing
Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video

IEEE Transactions on Circuits and Systems for Video Technology

Bayesian learning of finite generalized Gaussian mixture models on images

Signal Processing
Bayesian marginal statistics for speech enhancement using log Gabor wavelet

International Journal of Speech Technology
Bayesian learning of generalized gaussian mixture models on biomedical images

ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

Statistical model of subband wavelet coefficients fitted with generalized Gaussian density (GGD) has been widely used into image retrieval, classification, segmentation, denoising, and analysis/synthesis. Moment estimation is very simple method for estimation of the GGD parameters, but it is not sharp. Maximum likelihood estimation is obtained by solving the transcendental equation. Unfortunately, the equation has not analytical solution, and has to be solved numerically. We discover that Newton-Raphson iteration converges very slowly for the transcendental equation. To speed up convergence, we present Regula-Falsi method in place of Newton-Raphson method. The experiments show that Regula-Falsi method reaches the accuracy of the order 10^-^7 in three iterations.