Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-0-387-74161-1_37 | International Federation for Information Processing |
Keywords | Field | DocType |
image compression,neural network,peak signal to noise ratio | ENCODE,Data compression ratio,Pattern recognition,Computer science,Artificial intelligence,Artificial neural network,Data compression,Image compression,Machine learning | Conference |
ISSN | Citations | PageRank |
1571-5736 | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Mérida Casermeiro | 1 | 22 | 5.38 |
| Domingo López-Rodríguez | 2 | 55 | 9.24 |
| Juan Miguel Ortiz-de-lazcano-lobato | 3 | 68 | 11.59 |