Paper Info
Title
Optimal Quantization Noise Allocation and Coding Gain in Transform Coding with Two-Dimensional Morphological Haar Wavelet
Abstract
This paper analytically formulates both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform. The two-dimensional morphological Haar wavelet transform has been proposed as a nonlinear wavelet transform. It has been anticipated for application to nonlinear transform coding. To utilize a transformation to transform coding, both the optimal quantization noise allocation ratio and the coding gain of the transformation should be derived beforehand regardless of whether the transformation is linear or nonlinear. The derivation is crucial for progress of nonlinear transform image coding with nonlinear wavelet because the two-dimensional morphological Haar wavelet is the most basic nonlinear wavelet. We derive both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform by introducing appropriate approximations to handle the cumbersome nonlinear operator included in the transformation. Numerical experiments confirmed the validity of formulations.
Year
DOI
Venue
2005
10.1093/ietisy/e88-d.3.636
IEICE Transactions
Keywords
Field
DocType
two-dimensional morphological haar wavelet,basic nonlinear wavelet,numerical experiment,transform coding,coding gain,cumbersome nonlinear operator,paper analytically,nonlinear wavelet,appropriate approximation,optimal quantization noise allocation,quantization noise
Harmonic wavelet transform,Pattern recognition,Computer science,Second-generation wavelet transform,Discrete wavelet transform,Artificial intelligence,Haar wavelet,Stationary wavelet transform,Wavelet packet decomposition,Wavelet transform,Wavelet
Journal
Volume
Issue
ISSN
E88-D
3
0916-8532
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Yasunari Yokota133.82
Xiaoyong Tan200.68