The color quantization problem solved by swarm-based operations

Delgado, María Luisa Pérez

doi:10.1007/s10489-018-1389-6

Cited by 13 publications

(9 citation statements)

References 59 publications

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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%

“…Four splitting methods have been applied: Wu's method (WU) [26], Octree (OC) [28], Median-cut (MC) [24] and the Variance-based method (VB) [25]. In addition, five clustering-based methods were also used: Neuquant (NQ) [33], K-means (KM), the Shuffled-frog leaping algorithm (SFLA) [44], the Artificial bee colony algorithm combined with ATCQ (ABC+ATCQ) [42] and the Firefly algorithm combined with ATCQ (ATCQ+FA) [43]. To complete the comparison, the WATCQ method described in [52] was considered, which applies ATCQ to the partition defined by the Wu's method.…”

Section: Tables A6-a9mentioning

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%

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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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Section: The Bs+itatcq Methodsmentioning

confidence: 99%

Section: Tables A6-a9mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Mixed Method with Effective Color Reduction

Delgado

2020

Applied Sciences

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“…The Artificial Bee Colony (ABC) algorithm, developed by Karaboga [53,54], imitates the foraging behaviour of honey bees, and has been applied to solve several optimisation problems [55].…”

Section: Artificial Bee Colonymentioning

confidence: 99%

A Proposal for the Organisational Measure in Intelligent Systems

Román

Delgado

2020

Applied Sciences

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The collaboration within organisations and among organisations is a fundamental concept in the attainment of the overall objectives pursued by an enterprise network in human companies. Swarm systems are intelligent systems that show collaboration within the system; moreover, some models, such as multiple ant colonies, show the collaboration of several systems to achieve a global goal. The collaboration in this type of system optimises the achievement of the overall objectives as in an enterprise network in human organisations. Being able to measure this collaboration allows establishing a relationship between the improvement in the results of the system and the degree of collaboration, both at the level of specialisation of each element of the system and the systems as a whole. The performance of a swarm system depends on the number of members in many cases, so that if we can establish a measure of specialisation and collaboration, we could tipify and classify these systems in terms of the efficiency and the realiability to perform different tasks.

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“…Color quantization carrying out an important role in color image segmentation, image and video compression, color texture analysis, etc., given the characteristics of the human visual system (HVS), color quantization techniques require attention to the impacts of minimal variation [10]. In another context, at reducing the image colors, it is not possible to fully preserve every detail of the image, as a result of the use of a little number of pixels to create the output image, this may lead to a loss of part of the details thus reducing size [11].…”

Section: Introductionmentioning

confidence: 99%

Color Quantization Based Artificial Bee Colony and Training Tools

Reda¹,

Ibrahim²

2022

IJCDS

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Color quantization addressed as a useful method to help with limited disk capacity, network bandwidth, monitors true resolution. It works in reducing the true colors to the colors that the human eyes can detect and don't change the scene. In this paper a new method for color image quantization is proposed that depends on hybrid artificial tools. It consists of three main steps, the first is creating K colors to depend on them in quantizing the required colors in this step k-means algorithm is used starting from N colors. Then Artificial Bee Colony algorithm is used to improve the center of each cluster (color) which is combined to k-means algorithm as part of its optimization. Finally, the proposed color palette is used to quantize the image depending on the result of hybrid approach and apply to k-means. Two metrics are used to optimized the method (MSE, PSNR), both of them show good results for the quality of quantized images, as well as good human perception and good running time.

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The color quantization problem solved by swarm-based operations

Cited by 13 publications

References 59 publications

A Mixed Method with Effective Color Reduction

A Mixed Method with Effective Color Reduction

A Proposal for the Organisational Measure in Intelligent Systems

Color Quantization Based Artificial Bee Colony and Training Tools

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