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].
2
Dostęp do pełnego tekstu na zewnętrznej witrynie WWW
It is shown that the discrete time dynamical system defined by the Oja-RLS algorithm is stable in the closed ring K(0,9/8) - K(0,8/9) if only the initial gain B0 is bounded by (2B)-1, where B=b2 and b is the bound for the learning sequence. It is rigorously proved that automatically computed gains Bn in Oja-RLS scheme converge to zero with the rate 1/n, almost surely.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.