Adaptive learning method in self-organizing map for edge preserving vector quantization

Author

Kim, Y.K. ; Ra, J.B.

Author_Institution

Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea

Volume

Issue

fYear

1995

fDate

1/1/1995 12:00:00 AM

Firstpage

278

Lastpage

280

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 are determined according to the image block activity. The simulation result of 4×4 vector quantization for 512×512 image coding demonstrates the feasibility of the proposed method

Keywords

image coding; learning (artificial intelligence); self-organising feature maps; vector quantisation; 262144 pixel; 512 pixel; Kohonen´s self-organizing map; adaptive learning; coded image; edge degradation; edge preserving vector quantization; image block activity; Algorithm design and analysis; Clustering algorithms; Degradation; Discrete cosine transforms; Image coding; Iterative algorithms; Iterative methods; Learning systems; Neural networks; Vector quantization;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.363425

Filename

363425

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1242127