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

Delgado, María Luisa Pérez; Gallego, Jesús Ángel Román

doi:10.1109/access.2019.2937934

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…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. The results reported for each method correspond to 20 independent tests for each image and palette size.…”

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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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confidence: 99%

A Mixed Method with Effective Color Reduction

Delgado

2020

Applied Sciences

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“…To improve the overall image of the product and reduce the errors in the evaluation of and decision-making for color schemes in product development, and increasing number of design companies and designers have paid attention to the quantitative evaluation of color schemes. The emergence of eye movement tracking technology makes it possible to conduct an objective evaluation of color matching design schemes based on data quantification [2], [3]. With the use and development of eye movement tracking technology, researchers have gradually realized eye tracking and data recording for the evaluation process.…”

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confidence: 99%

Color Scheme Compensatory Evaluation Method Based on Eye Movement Tracking Technology

et al. 2020

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In the process of product color scheme evaluation, subjective evaluation is liable to mislead evaluation and decision-making. To conduct a more objective quantitative evaluation for color schemes, a compensatory evaluation method for color scheme based on eye movement tracking technology is proposed in this paper. In this method, four types of eye movement experimental data-fixation time, number of fixation points, number of fixation times, and first fixation time-are collected in the area of interest (AOI) from the experimental sample by eye movement tracking technology. SPSS software is employed to test the reliability of the experimental data, and the value assignment method is used to perform compensation processing on the experimental data, so that the final evaluation result can be obtained with the assignment calculation. Taking the evaluation of the disguise color scheme of a camouflage uniform as an example, the operation process of the method is demonstrated, and the effectiveness of the method is verified with the questionnaire. The results show that the color scheme compensatory evaluation method based on eye movement tracking technology can achieve an effective evaluation of color schemes. This method realizes the quantification of and data compensation for color scheme evaluation, making the evaluation results more scientific and accurate and providing a reference basis for color scheme designers in evaluation and decision-making. INDEX TERMS Eye movement tracking technology, entropy weighting method, compensatory evaluation, color matching evaluation method.

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“…(metaheuristic) (SFLA)• WU combined with ATCQ34 (metaheuristic) (WUATCQ)• Particle swarm optimization combined with ATCQ35 (metaheuristic) (PSOATCQ)• BS combined with ITATCQ 36 (metaheuristic) (BSITATCQ)• Iterative ATCQ 37 (metaheuristic) (ITATCQ)• Iterative online k-means algorithm38 (partitional) (IOKM)These algorithms represent the CQ work published between 1980 and 2022. (For a modern survey of CQ, see Ref.…”

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cq100: a high-quality image dataset for color quantization research

Celebi

Delgado

2023

J. Electron. Imag.

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.Color quantization (cq) is a classical image processing operation that reduces the number of distinct colors in a given image. Although the idea of cq dates back to the early 1970s, the first true cq algorithm, median-cut, was proposed later in 1980. Since then, hundreds of publications have investigated the topic of cq, proposing dozens of algorithms. A vast majority of these publications demonstrate their results on small datasets, containing a handful of images of mixed quality. Furthermore, the reproducibility of cq research is often limited due to the use of private test images or public test images with multiple non-identical copies on the World Wide Web or restrictive licenses. To address these problems, we curated a large, diverse, and high-quality dataset of 24-bit color images called cq100 and released it under a permissive license. We present an overview of cq100 and demonstrate its use in comparing cq algorithms.

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

Cited by 8 publications

References 53 publications

A Mixed Method with Effective Color Reduction

A Mixed Method with Effective Color Reduction

Color Scheme Compensatory Evaluation Method Based on Eye Movement Tracking Technology

cq100: a high-quality image dataset for color quantization research

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