2026-09-15T14:38:19Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1610472024-12-12T01:01:45Zcom_10366_159928com_10366_4512com_10366_3823col_10366_159929
Gestión del Repositorio Documental de la Universidad de Salamanca
author
Pérez Delgado, María Luisa
author
Román Gallego, Jesús Ángel
2024-12-11T08:51:09Z
2024-12-11T08:51:09Z
2019
M. -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.2019.2937934
2169-3536
http://hdl.handle.net/10366/161047
10.1109/ACCESS.2019.2937934
[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.
eng
Attribution-NonCommercial-NoDerivs 3.0 Unported
Color image analysis
Quantization
Clustering algorithms
Artificial intelligence
Color quantization
A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants
info:eu-repo/semantics/article
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
URL
https://gredos.usal.es/bitstream/10366/161047/1/CIMET_PerezDelgadoML_RomanJA_HybridCQ_GOBP_ants.pdf
File
MD5
8d8db191903291bfe8236655d9e333f3
22164610
application/pdf
CIMET_PerezDelgadoML_RomanJA_HybridCQ_GOBP_ants.pdf
URL
https://gredos.usal.es/bitstream/10366/161047/4/CIMET_PerezDelgadoML_RomanJA_HybridCQ_GOBP_ants.pdf.txt
File
MD5
92cbd3f36cbf23a7f82918c2705b1c27
74616
text/plain
CIMET_PerezDelgadoML_RomanJA_HybridCQ_GOBP_ants.pdf.txt