RT Book, Section
T1 Two methods for image compression/reconstruction using OWA operators
A1 Bustince, Humberto
A1 Paternain, D.
A1 Calvo, Tomasa
A1 De Baets, Bernard
A1 Fodor, János
A1 Mesiar, Radko
A1 Montero De Juan, Francisco Javier
A1 Pradera, A.
A2 Yager, Ronald R.
A2 Kacprzyk, Janusz
A2 Beliakov, Gleb
AB 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.
PB Springer Berlin Heidelberg
SN 978-3-642-17909-9
YR 2011
FD 2011
LK https://hdl.handle.net/20.500.14352/45566
UL https://hdl.handle.net/20.500.14352/45566
LA dan
NO Bustince, H., Paternain, D., De Baets, B., Calvo, T., Fodor, J., Mesiar, R., Montero, J., Pradera, A.: Two Methods for Image Compression/Reconstruction Using OWA Operators. En: Yager, R.R., Kacprzyk, J., y Beliakov, G. (eds.) Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. pp. 229-253. Springer Berlin Heidelberg, Berlin, Heidelberg (2011)
NO Ministerio de Educación, Formación Profesional y Deportes (España)
NO Comisión Interministerial de Ciencia y Tecnología (España)
DS Docta Complutense
RD 11 abr 2025