Affiliations: Department of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warszawa, Poland. [email protected] | Department of Informatics, Technical University of Białystok, Białystok, Poland | Institute of Mathematics Polish Academy of Sciences Warsaw, Poland
Note: [] Address for correspondence: Department of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.
Note: [] Address for correspondence: Department of Informatics, Technical University of Białystok, Białystok, Poland
Note: [] Address for correspondence: Institute of Mathematics, Polish Academy of Sciences, Warsaw, Poland
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].
Keywords: Image compression, JPEG standard, KLT transform, Principal Component Analysis, Oja neural algorithm