An iterative method to improve the results of ant-tree algorithm applied to colour quantisation

Delgado, María Luisa Pérez

doi:10.1504/ijbic.2018.10015516

Cited by 5 publications

(9 citation statements)

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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%

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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%

“…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%

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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“…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%

“…This article proposes the introduction of an improved version of the BS+ATCQ algorithm, by replacing the ATCQ algorithm with the ITATCQ algorithm. The ITATCQ method is based on the ATCQ algorithm but generates better quantized images [40]. Therefore, it is expected that the result obtained by combining ITATCQ with BS will generate better images than those obtained using the initial combination of BS and ATCQ.…”

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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“…In recent years, a large number of bio-inspired optimization algorithms have been proposed, such as the genetic algorithm (GA) [16,17], bat algorithm (BA) [18][19][20][21], differential evolution (DE) [20,21], firefly algorithm (FA) [22], artificial bee colony (ABC) [23,24], cuckoo algorithm (CS) [25][26][27], and so on [28][29][30][31][32], which have been applied to various fields, including the optimization problems [33][34][35][36][37], practical application problems [38][39][40][41], LEACH (Low Energy Adaptive Clustering Hierarchy) protocol optimization [42], and so on [43][44][45][46]. Particularly, Cui et al [47] designed an optimization method based on the bat algorithm to optimize the random Gaussian observation matrix and reduced the reconstruction error.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

et al. 2019

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Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.

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An iterative method to improve the results of ant-tree algorithm applied to colour quantisation

Cited by 5 publications

References 0 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

Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

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