DocumentCode
1904752
Title
Image compression using topological maps and MLP
Author
Burel, Gilles ; Catros, Jean-Yves
Author_Institution
Thomson CSF, Cesson-Sevigne, France
fYear
1993
fDate
1993
Firstpage
727
Abstract
An image compression technique is proposed in which a multilayer perceptron (MLP) predictor takes advantage of the topological properties of the Kohonen algorithm. The Kohonen algorithm creates a code-book which is used for vector quantization of the source image. Then, an MLP is trained to predict references to code-book, allowing further compression. Even with difficult images, the result is a reduction of 15% to 20% of the bit rate compared with classical vector quantization techniques, for the same quality of decoded images
Keywords
feedforward neural nets; image coding; image processing; topology; vector quantisation; Kohonen algorithm; code-book; image coding; image compression; image processing; multilayer perceptron; topological maps; vector quantization; Backpropagation; Bandwidth; Bit rate; Books; Decoding; Image coding; Image storage; Multilayer perceptrons; Transform coding; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298645
Filename
298645
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