Color image quantization using flower pollination algorithm

Lei, Mengyi; Zhou, Yongquan; Luo, Qifang

doi:10.1007/s11042-020-09680-1

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…PSNR has an inverse relationship with the error, PSNR increases lead to a decrease in error, and the greater the signal. In figure (13),it is clear that increasing number of colors in a certain image gives more accuracy for the resulting image as well as increase the processing time. Figure (15) shows the results related to the compression ratio between the original image and the resulting image.…”

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

“…Lei et al [13] suggests a new implementation for the Flower pollination swarm-based algorithm in the scope of image processing for solving CQ problem, in which the MSE is used as a parameter for the color quantization optimization problem to be solved. His suggested Flower pollination algorithm for CQ was validated by comparison to K-Means and other swarm intelligence techniques.…”

Section: Related Workmentioning

confidence: 99%

“…When more detailed information is saved, this means an increase in the number of colors, which means more transmission time and storage [12]. It can be concluded that number of founded colors and the quantity of detailed information represent an ambivalence, which leads to a difficult problem in NP for color quantification [13].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Color Quantization Based Artificial Bee Colony and Training Tools

Reda¹,

Ibrahim²

2022

IJCDS

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“…In wireless sensor networking, FPA is used for problems of layouts of nodes (Nguyen et al, 2019) and antenna design problems (Salgotra et al, 2020). FPA shows supremacy in the domain of image processing and a sensor for problems such as coloring (Lei et al, 2020), medical image segmentation (Wang et al, 2015), person identification based on EEG (Alyasseri et al, 2018) and many more. FPA is used to find solutions for hard levels of games such as Sudoku .…”

Section: IImentioning

confidence: 99%

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

Sundarkar¹,

Adamuthe²

2021

JSR

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The 01 knapsack problem is a combinatorial optimization problem where objective is to maximum profit. It can be considered as a resource allocation problem where the decision is to pick the most important items. Knapsack problem has a large range of applications in many domains. This paper presents three variations of FPA influenced by concept of crossover and mutation operator in genetic algorithms. The three variations, named FPA with crossover and mutation (Version 1), FPA with crossover (Version 2), FPA with mutation (Version 3) for solving 01 knapsack problem are presented. Comparison of variations is done with basic FPA algorithms and other algorithms for three datasets of a single dimension of 01 knapsack problem. The results show that improved FPA has better results than basic FPA. The results are optimal for smaller instances and with increase in number of objects results are closer to optimal but not optimal.

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Quick flower pollination algorithm (QFPA) and its performance on neural network training

Kaya

2022

Soft Comput

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Color image quantization using flower pollination algorithm

Cited by 10 publications

References 35 publications

Color Quantization Based Artificial Bee Colony and Training Tools

Color Quantization Based Artificial Bee Colony and Training Tools

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

Quick flower pollination algorithm (QFPA) and its performance on neural network training

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