Paper Info
Title
Color image vector quantization using an enhanced self-organizing neural network
Abstract
In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.
Year
DOI
Venue
2004
10.1007/978-3-540-30497-5_172
CIS
Keywords
Field
DocType
competitive learning method,entire image,compression ratio,color image,compression method,lbg algorithm,good data,image compression,high data,enhanced self-organizing neural network,color image vector quantization,data compression,competitive learning,self organization,neural network
Competitive learning,Data compression ratio,Computer science,Algorithm,Compression ratio,Vector quantization,Data compression,Image compression,Color image,Lossless compression
Conference
Volume
ISSN
ISBN
3314
0302-9743
3-540-24127-2
Citations 
PageRank 
References 
2
0.42
4
Authors
2
Name
Order
Citations
PageRank
kwangbaek kim111043.94
Abhijit S. Pandya210822.91