Empirical data decomposition and its applications in image compression

被引：0

作者：

Deng Jiaxian ^{[1
]}

Wu Xiaoqin ^{[1
,2
]}

机构：

[1] Hainan Univ, Informat Sci & Technol Coll, Haikou 570228, Peoples R China

[2] Beijing Jiaotong Univ, Elect Informat Engn Coll, Beijing 100044, Peoples R China

来源：

JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2007年 / 18卷 / 01期

关键词：

image processing; image compression; empirical data decomposition; non-stationary; nonlinear; data decomposition framework;

D O I：

10.1016/S1004-4132(07)60069-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A nonlinear data analysis algorithm, namely empirical data decomposition (EDD) is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation (EBCOT) Simulation results show that EDD is more suitable for non-stationary image data compression.

引用

页码：164 / 170

页数：7

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