Multi-stage vector quantization based on the self-organization feature maps

Document Type

Conference Proceeding

Publication Date

11-1-1989

Abstract

A neural network clustering algorithm, termed Self-Organization Feature Maps(SOFM) proposed by Kohoneni, is used to design a vector quantizer. The SOFM algorithm differs from the LBG algorithm in that the former forms a codebook adaptively but not iteratively. For every input vector, the weight between the input node and the corresponding output node is updated by encouraging a shift toward the center of gravity in the due influence region. Some important properties are discussed, demonstrated by examples, and compared with the LBG algorithm. Based on this clustering algorithm, a very practical image sequence coding scheme is proposed, which consists of two cascade neural networks. The first stage network is adapted with every frame so that the coder can quickly track the local changes in the picture. Simulation results have shown that quite robust performance can be achieved with high signal to noise ratio using the absolute value distortion measure. Additionally, the cascade scheme substantially reduces the computation complexity. © 1989 SPIE.

Identifier

0024932237 (Scopus)

Publication Title

Proceedings of SPIE the International Society for Optical Engineering

External Full Text Location

https://doi.org/10.1117/12.970114

e-ISSN

1996756X

ISSN

0277786X

First Page

1046

Last Page

1055

Volume

1199

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