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.
This work has been partially supported by Junta de Andalucía project number P06-TIC-01615.
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© 2007 International Federation for Information Processing
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Mérida-Casermeiro, E., López-Rodríguez, D., Ortiz-de-Lazcano-Lobato, J.M. (2007). Image Compression with Competitive Networks and Pre-fixed Prototypes. In: Boukis, C., Pnevmatikakis, A., Polymenakos, L. (eds) Artificial Intelligence and Innovations 2007: from Theory to Applications. AIAI 2007. IFIP The International Federation for Information Processing, vol 247. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74161-1_37
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DOI: https://doi.org/10.1007/978-0-387-74161-1_37
Publisher Name: Springer, Boston, MA
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