Colour quantization with Ant-tree

Delgado, María Luisa Pérez

doi:10.1016/j.asoc.2015.07.048

Cited by 25 publications

(20 citation statements)

References 39 publications

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“…The ATCQ algorithm is a color quantization method based on the Ant-tree algorithm [23]. It has been observed that some species of ants can avoid obstacles or cross empty spaces by connecting their bodies to build structures.…”

Section: The Ant-tree For Color Quantization Methodsmentioning

confidence: 99%

“…Each subtree defines a color of the quantized palette and that color is used to represent all the ants of the subtree in the quantized image. This method was compared to other well-known color quantization methods, obtaining better images than Octree, Mediancut and Variance-based methods [23]. When compared to GOBP, ATCQ only generates better images in some cases, as reported in the article which proposed the second method.…”

Section: Introductionmentioning

confidence: 91%

“…Other solutions focus on the pixels of the image and separate them into groups. This is the approach applied by color quantization methods such as Neuquant [22] and Ant-tree for color quantization (ATCQ) [23], and also by other solutions that use non-specific methods of color quantization, such as K-means [24], Particle swarm optimization [25] and Artificial bee colony [26] algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The ATCQ method mimics the behavior of a set of ants that build a tree that allows an image to be represented using a reduced number of colors [23]. The ants, which represent the pixels of the image, connect to the tree taking into account the similarity among the colors they represent.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

Delgado

Gallego

2019

IEEE Access

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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.INDEX TERMS Artificial intelligence, clustering methods, image processing.

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Section: The Ant-tree For Color Quantization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

Delgado

Gallego

2019

IEEE Access

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“…In their method, the color palette is derived by the simulated annealing algorithm in order to provide good image quality according to S-CIELAB. In 2015, a color quantization method, called ATCQ, was proposed by Pérez-Delgado [9]. ATCQ adapted some of the features of the basic Ant-tree to obtain a quicker algorithm for color image quantization.…”

Section: Introductionmentioning

confidence: 99%

Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Huang

2020

Symmetry

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Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.

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Population-Based Methods to Reduce the Colors of an Image

Delgado

Gallego

2022

Advances in Intelligent Systems and Computing

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Colour quantization with Ant-tree

Cited by 25 publications

References 39 publications

A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Population-Based Methods to Reduce the Colors of an Image

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