Low-complexity rounded KLT approximation for image compression

被引:0
|
作者
Anabeth P. Radünz
Fábio M. Bayer
Renato J. Cintra
机构
[1] Universidade Federal de Pernambuco,Programa de Pós
[2] Universidade Federal de Santa Maria,Graduação em Estatística
[3] Universidade Federal de Pernambuco,Departamento de Estatística and LACESM
[4] University of Calgary,Signal Processing Group, Departamento de Estatística
[5] Florida International University,Department of Electrical and Computer Engineering
来源
关键词
Approximate KLT; Image compression; Karhunen–Loève transform; Low-complexity transforms;
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摘要
The Karhunen–Loève transform (KLT) is often used for data decorrelation and dimensionality reduction. Because its computation depends on the matrix of covariances of the input signal, the use of the KLT in real-time applications is severely constrained by the difficulty in developing fast algorithms to implement it. In this context, this paper proposes a new class of low-complexity transforms that are obtained through the application of the round function to the elements of the KLT matrix. The proposed transforms are evaluated considering figures of merit that measure the coding power and distance of the proposed approximations to the exact KLT and are also explored in image compression experiments. Fast algorithms are introduced for the proposed approximate transforms. It was shown that the proposed transforms perform well in image compression and require a low implementation cost.
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页码:173 / 183
页数:10
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