The Modified Oja-RLS Algorithm, Stochastic Convergence Analysis and Application for Image Compression
Władysław Skarbek , Adam Pietrowcew , Radosław Sikora
AbstractThe 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].
|Journal series||Fundamenta Informaticae, ISSN 0169-2968|
|Publication indicators||: 2006 = 0.586 (2) - 2007=0.774 (5)|
|Citation count*||3 (2018-06-18)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.