Color quantization with Particle swarm optimization and artificial ants

Delgado, María Luisa Pérez

doi:10.1007/s00500-019-04216-8

Cited by 16 publications

(14 citation statements)

References 54 publications

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“…Therefore, the value selected for the parameter Thr of the proposed algorithm was 10. In 2020, a color image quantization algorithm, identified as PSO+ATCQ-3, that combines particle swarm optimization and artificial ants was proposed by Pérez-Delgado [17]. The quality of the quantized images produced by PSO+ATCQ-3 is better than that produced by some well-known color image quantization methods.…”

Section: Resultsmentioning

confidence: 99%

“…The quality of the quantized images produced by PSO+ATCQ-3 is better than that produced by some well-known color image quantization methods. The PSO+ATCQ-3 algorithm was implemented in C language and executed on a PC running under the Linux In 2020, a color image quantization algorithm, identified as PSO+ATCQ-3, that combines particle swarm optimization and artificial ants was proposed by Pérez-Delgado [17]. The quality of the quantized images produced by PSO+ATCQ-3 is better than that produced by some well-known color image quantization methods.…”

Section: Resultsmentioning

confidence: 99%

“…The quality of the quantized images produced by PSO+ATCQ-3 is better than that produced by some well-known color image quantization methods. The PSO+ATCQ-3 algorithm was implemented in C language and executed on a PC running under the Linux operating system with an I7 processor (2 GHz) and 16 GBytes of RAM [17]. The experimental results obtained by PSO+ATCQ-3 are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…Pérez-Delgado [16] employed the shuffled-frog leaping algorithm for color image quantization. Pérez-Delgado [17] solved the color quantization problem by combining the particle swarm optimization algorithm with the Ant-tree.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Palette Generation Method for Color Image Quantization

Huang

2021

Applied Sciences

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This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The quality of the quantized images produced by PSO+ATCQ-3 is better than that produced by some well-known color image quantization methods. The PSO+ATCQ-3 algorithm was implemented in C language and executed on a PC running under the Linux operating system with an I7 processor (2 GHz) and 16 GBytes of RAM [17]. The experimental results obtained by PSO+ATCQ-3 are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Palette Generation Method for Color Image Quantization

Huang

2021

Applied Sciences

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“…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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Color quantization with Particle swarm optimization and artificial ants

Cited by 16 publications

References 54 publications

An Efficient Palette Generation Method for Color Image Quantization

An Efficient Palette Generation Method for Color Image Quantization

A Mixed Method with Effective Color Reduction

Population-Based Methods to Reduce the Colors of an Image

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