Artificial ants and fireflies can perform colour quantisation

Delgado, María Luisa Pérez

doi:10.1016/j.asoc.2018.08.018

Cited by 14 publications

(8 citation statements)

References 40 publications

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“…Some of the color quantization methods described in Section III were applied to the test images, in order to compare their results with those obtained by WATCQ. The following methods were considered: Variance-based method [21], Median-cut [19], Octree [18], Binary splitting [20], BS + ATCQ [33], Neuquant [22], LBG [57], K-means [24], Particle swarm optimization (PSO) [46], ATCQ + FA [32], and ITATCQ [31]. LBG is based on the vector quantization method proposed in [57], which has also been applied to color quantization.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, three color quantization methods based on ATCQ have been proposed: ITATCQ [31], ATCQ + FA [32] and BS + ATCQ [33]. ITATCQ applies the ATCQ operations iteratively in order to improve the quality of the quantized palette.…”

Section: Overview Of Color Quantization Methodsmentioning

confidence: 99%

“…Many color quantization methods combine previously existing techniques [27]- [30], [32]- [40], [46], [47]. These solutions take advantage of the characteristics of the methods that combine to obtain better images or to accelerate the operations.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Overview Of Color Quantization Methodsmentioning

confidence: 99%

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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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“…There are many color quantization techniques that combine other existing methods [29,31,32,34,35,37,[41][42][43]46]. Two main characteristics are considered when defining a color quantization method: the quality of the quantized image and the time required to obtain such image.…”

Section: The Bs+itatcq Methodsmentioning

confidence: 99%

“…Another group of algorithms based on this approach are those that use a swarm of individuals to solve a complex problem. Some methods of this type applied to color quantization are the Particle swarm optimization algorithm [37,38], the Ant-tree for color quantization (ATCQ) method [39], the Iterative ant-tree for color quantization (ITATCQ) method [40], the Artificial bee colony algorithm combined with K-means [41], the Artificial bee colony algorithm combined with ATCQ [42], the Firefly algorithm combined with ATCQ [43] and the Shuffled-frog leaping algorithm [44]. Other swarm-based methods applied to color quantization are summarized in [45].…”

Section: Introductionmentioning

confidence: 99%

A Mixed Method with Effective Color Reduction

Delgado

2020

Applied Sciences

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This article presents a color quantization technique that combines two previously proposed approaches: the Binary splitting method and the Iterative ant-tree for color quantization method. The resulting algorithm can obtain good quality images with low time consumption. In addition, the iterative nature of the proposed method allows the quality of the quantized image to improve as the iterations progress, although it also allows a good initial image to be quickly obtained. The proposed method was compared to 13 other color quantization techniques and the results showed that it could generate better quantized images than most of the techniques assessed. The statistical significance of the improvement obtained using the new method is confirmed by applying a statistical test to the results of all the methods compared.

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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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Artificial ants and fireflies can perform colour quantisation

Cited by 14 publications

References 40 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

A Mixed Method with Effective Color Reduction

Population-Based Methods to Reduce the Colors of an Image

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