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dc.contributor.authorPérez Delgado, María Luisa 
dc.contributor.authorRomán Gallego, Jesús Ángel 
dc.date.accessioned2024-12-11T08:51:09Z
dc.date.available2024-12-11T08:51:09Z
dc.date.issued2019
dc.identifier.citationM. -L. Pérez-Delgado and J. Á. Román Gallego, "A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants," in IEEE Access, vol. 7, pp. 128714-128734, 2019, doi: 10.1109/ACCESS.2019.2937934es_ES
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10366/161047
dc.description.abstract[EN]A color quantization technique that combines the operations of two existing methods is proposed. The first method considered is the Greedy orthogonal bi-partitioning method. This is a very popular technique in the color quantization field that can obtain a solution quickly. The second method, called Ant-tree for color quantization, was recently proposed and can obtain better images than some other color quantization techniques. The solution described in this article combines both methods to obtain images with good quality at a low computational cost. The resulting images are always better than those generated by each method applied separately. In addition, the results also improve those obtained by other well-known color quantization methods, such as Octree, Median-cut, Neuquant, Binary splitting or Variance-based methods. The features of the proposed method make it suitable for real-time image processing applications, which are related to many practical problems in diverse disciplines, such as medicine and engineering.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/*
dc.subjectColor image analysises_ES
dc.subjectQuantizationes_ES
dc.subjectClustering algorithmses_ES
dc.subjectArtificial intelligencees_ES
dc.subjectColor quantizationes_ES
dc.titleA Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Antses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://ieeexplore.ieee.org/document/8815696
dc.identifier.doi10.1109/ACCESS.2019.2937934
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported