Image Compression with Competitive Networks and Pre-fixed Prototypes

Authors

Enrique Mérida Casermeiro , Domingo López-Rodríguez , Juan Miguel Ortiz-de-Lazcano-Lobato

Published

1 January 2007

Publication details

IFIP International Federation for Information Processing, (247), pp. 339–346

Links

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Abstract

Image compression techniques have required much attention from the neural networks community for the last years. In this work we intend to develop a new algorithm to perform image compression based on adding some pre-fixed prototypes to those obtained by a competitive neural network. Prototypes are selected to get a better representation of the compressed image, improving the computational time needed to encode the image and decreasing the code-book storage necessities of the standard approach. This new method has been tested with some well-known images and results proved that our proposal outperforms classical methods in terms of maximizing peak-signal-to-noise-ratio values. © 2007 International Federation for Information Processing.

Citation

How to cite

E. M. Casermeiro, D. López-Rodríguez, and J. M. Ortiz-de-Lazcano-Lobato. “Image Compression with Competitive Networks and Pre-fixed Prototypes”. In: Artificial Intelligence and Innovations 2007: from Theory to Applications, Proceedings of the 4th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2007), 19-21 September 2007, Peania, Athens, Greece. Ed. by C. Boukis, A. Pnevmatikakis and L. Polymenakos. Vol. 247. IFIP. cited By 0. Springer, 2007, pp. 339-346. DOI: 10.1007/978-0-387-74161-1_37. URL: https://doi.org/10.1007/978-0-387-74161-1_37.

BibTeX

<pre><code>

@InProceedings{Casermeiro2007c, author = {Enrique Mérida Casermeiro and Domingo López-Rodríguez and Juan Miguel Ortiz-de-Lazcano-Lobato}, booktitle = {Artificial Intelligence and Innovations 2007: from Theory to Applications, Proceedings of the 4th {IFIP} International Conference on Artificial Intelligence Applications and Innovations {(AIAI} 2007), 19-21 September 2007, Peania, Athens, Greece}, title = {Image Compression with Competitive Networks and Pre-fixed Prototypes}, year = {2007}, editor = {Christos Boukis and Aristodemos Pnevmatikakis and Lazaros Polymenakos}, note = {cited By 0}, pages = {339–346}, publisher = {Springer}, series = {{IFIP}}, volume = {247}, abstract = {Image compression techniques have required much attention from the neural networks community for the last years. In this work we intend to develop a new algorithm to perform image compression based on adding some pre-fixed prototypes to those obtained by a competitive neural network. Prototypes are selected to get a better representation of the compressed image, improving the computational time needed to encode the image and decreasing the code-book storage necessities of the standard approach. This new method has been tested with some well-known images and results proved that our proposal outperforms classical methods in terms of maximizing peak-signal-to-noise-ratio values. © 2007 International Federation for Information Processing.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ifip12/CasermeiroLO07.bib}, document_type = {Conference Paper}, doi = {10.1007/978-0-387-74161-1_37}, journal = {IFIP International Federation for Information Processing}, keywords = {Image compression; Image quality, Classical methods; Competitive network; Competitive neural network; Compressed images; Computational time; Image compression techniques; Peak signal to noise ratio, Artificial intelligence}, source = {Scopus}, url = {https://doi.org/10.1007/978-0-387-74161-1_37}, }

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