The Modified Oja-RLS Algorithm, Stochastic Convergence Analysis and Application for Image Compression

Władysław Skarbek , Adam Pietrowcew , Radosław Sikora

Abstract

The results of theoretical analysis for stochastic convergence of the modified Oja-RLS learning rule are presented. The rule is used to find Karhunen Loeve Transform. Based on this algorithm, an image compression scheme is developed by combining approximated 2D KLT transform and JPEG standard quantization and entropy coding stages. Though 2D KLT transform is of higher complexity than 2D DCT, the resulting PSNR quality of reconstructed images is better even by 2[dB].
Author Władysław Skarbek RE
Władysław Skarbek,,
- The Institute of Radioelectronics
, Adam Pietrowcew RE
Adam Pietrowcew,,
- The Institute of Radioelectronics
, Radosław Sikora RE
Radosław Sikora,,
- The Institute of Radioelectronics
Journal seriesFundamenta Informaticae, ISSN 0169-2968
Issue year1998
Vol36
No4
Pages345-365
DOIDOI:10.3233/FI-1998-3644
URL http://dx.doi.org/10.3233/FI-1998-3644
Score (nominal)20
Publication indicators WoS Impact Factor: 2006 = 0.586 (2) - 2007=0.774 (5)
Citation count*3 (2018-02-19)
Cite
Share Share



* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
Back