### Abstract

In this chapter we address image compression by means of two alternative algorithms. In the first algorithm, we associate to each image an interval-valued fuzzy relation, and we build an image which is n times smaller than the original one, by using two-dimensional OWA operators. The experimental results show that, in this case, best results are obtained with ME-OWA operators. In the second part of the work, we describe a reduction algorithm that replaces the image by several eigen fuzzy sets associated with it. We obtain these eigen fuzzy sets by means of an equation that relates the OWA operators we use and the relation (image) we consider. Finally, we present a reconstruction method based on an algorithm which minimizes a cost function, with this cost function built by means of two-dimensional OWA operators.

Original language | English |
---|---|

Title of host publication | Recent Developments in the Ordered Weighted Averaging Operators |

Subtitle of host publication | Theory and Practice |

Editors | Ronald R. Yager, Janusz Kacprzyk, Gleb Beliakov |

Pages | 229-253 |

Number of pages | 25 |

DOIs | |

Publication status | Published - Feb 28 2011 |

### Publication series

Name | Studies in Fuzziness and Soft Computing |
---|---|

Volume | 265 |

ISSN (Print) | 1434-9922 |

### ASJC Scopus subject areas

- Computer Science (miscellaneous)
- Computational Mathematics

## Fingerprint Dive into the research topics of 'Two methods for image compression/reconstruction using OWA operators'. Together they form a unique fingerprint.

## Cite this

*Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice*(pp. 229-253). (Studies in Fuzziness and Soft Computing; Vol. 265). https://doi.org/10.1007/978-3-642-17910-5_12