A comparision between PCA neural networks and the JPEG standard for performing image compression

Author

Oliveira, Patricia R. ; Romero, Roseli F.

Author_Institution

SCE-ICMSC-USP, Sao Carlos, Brazil

fYear

1996

fDate

9-11 Dec 1996

Firstpage

112

Lastpage

116

Abstract

Principal component analysis (PCA), also called Karhunen-Loeve transform, is a statistical method for multivariate data analysis that can be used in particular to reduce the data set being considered. There are two approaches for performing PCA. The first utilizes the classical statistical method and the other, artificial neural networks. In this paper, neural networks that performing PCA are presented and used to realize tomographic image compression. The results obtained are compared to that obtained by using JPEG compression standard technique and show the usefulness of neural networks for performing image compression

Keywords

data compression; image coding; neural nets; statistical analysis; transforms; JPEG compression standard technique; Karhunen-Loeve transform; PCA neural networks; image compression; multivariate data analysis; principal component analysis; statistical method; tomographic image compression; Artificial neural networks; Data analysis; Discrete cosine transforms; Electronics packaging; Frequency domain analysis; Image coding; Neural networks; Principal component analysis; Statistical analysis; Transform coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetic Vision, 1996. Proceedings., Second Workshop on

Conference_Location

Sao Carlos

Print_ISBN

0-8186-8058-X

Type

conf

DOI

10.1109/CYBVIS.1996.629449

Filename

629449