ADAPTIVE LEARNING-METHOD IN SELF-ORGANIZING MAP FOR EDGE-PRESERVING VECTOR QUANTIZATION

Authors
KIM YK RA JB
Citation
Yk. Kim et Jb. Ra, ADAPTIVE LEARNING-METHOD IN SELF-ORGANIZING MAP FOR EDGE-PRESERVING VECTOR QUANTIZATION, IEEE transactions on neural networks, 6(1), 1995, pp. 278-280
Citations number
5
Categorie Soggetti
Computer Application, Chemistry & Engineering","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence","Computer Science Hardware & Architecture","Computer Science Theory & Methods
Journal title
IEEE transactions on neural networks → ACNP
ISSN journal
10459227
Volume
6
Issue
1
Year of publication
1995
Pages
278 - 280
Database
ISI
SICI code
1045-9227(1995)6:1<278:ALISMF>2.0.ZU;2-I
Abstract
The Kohonen's Self-Organizing Map algorithm for vector quantization of images is modified to reduce the edge degradation in the coded image. The learning procedure is performed by adaptive learning rates that a re determined according to the image block activity. The simulation re sult of 4x4 vector quantization for 512x512 image coding demonstrates the feasibility; of the proposed method.