A Color Image Quantization Algorithm Based on Particle Swarm Optimization (original) (raw)

B13 A NOVEL SWARM BASED APPROACH TO COLOR QUANTIZATION

Color Quantization (CQ) which is considered as an NP-Hard optimization problem is a process that aims to reduce the number of distinct colors in a given image. The goal of CQ is to represent the image with fewer colors and less data while trying to keep the distortion of the final image as low as possible. In this paper, a novel approach based on Intelligent Water Drops (IWD) Algorithm is proposed for CQ problem. The proposed algorithm IWD-CQ is compared with five conventional methods of CQ; Uniform, Popularity, Median-Cut, Octree and K-Means, in terms of visual quality of the quantized image. The comparison results show the superiority of IWD-CQ to the all tested methods in this study.

Color quantization using modified artificial fish swarm algorithm

AI 2011: Advances in …, 2011

Color quantization (CQ) is one of the most important techniques in image compression and processing. Most of quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.

Color quantization with clustering by F-PSO-GA

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of GA and Particle Swarm Optimization algorithms with FCM algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms.

On Color Image Quantization by the K-Means Algorithm

2004

In this paper we show the main properties of k-means algorithm as a tool for color image quantization. All experiments have been carried out on color images with different number of unique colors and different colorfulness. We have tested the influence of methods of determination of initial cluster centers, of choice of distance metric, of choice of color space. In our tests we have used two dimensions of palette (256,16) and three different measures for quantization errors. The results of k-means technique have been compared with quantized im- ages from commercial programs.

A Hybrid Approach for Color Image Quantization. Using K-Means and Firefly Algorithms

World Academy of Science, Engineering and Technology, 2013

Color Image quantization (CQ) is an important problem in computer graphics, image and processing. The aim of quantization is to reduce colors in an image with minimum distortion. Clustering is a widely used technique for color quantization; all colors in an image are grouped to small clusters. In this paper, we proposed a new hybrid approach for color quantization using firefly algorithm (FA) and K-means algorithm. Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. The proposed method can overcome the drawbacks of both algorithms such as the local optima converge problem in K-means and the early converge of firefly algorithm. Experiments on three commonly used images and the comparison results shows that the proposed algorithm surpasses both the base-line technique k-means clustering and original firefly algorithm

Comparison and optimization of methods of color image quantization

IEEE Transactions on Image Processing, 1997

Color image quantization is the process of reducing the number of colors in a digital color image has been widely studied for the last fteen years. In this paper the di erent steps of clustering methods are studied. The methods are compared step by step and some optimizations of the algorithms and data structures are given. A new color space called H1H2H3 is introduced which improves quantization heuristics. A low-cost quantization scheme is proposed.

A Hybrid Color Image Quantization Algorithm Based on k-Means and Harmony Search Algorithms

Applied Artificial Intelligence, 2016

Color quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.

Fast Color Quantization by K-Means Clustering Combined with Image Sampling

Symmetry

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

A GENETIC C-MEANS CLUSTERING ALGORITHM APPLIED TO COLOR IMAGE QUANTIZATION

This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is elaborated.

Color Image Quantization based on Bacteria Foraging Optimization

International Journal of Computer Applications, 2011

Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary if the display on which a specific image is presented works with less colors than the original image. While a lot of color reduction techniques exist in the literature, they are mainly designed for image compression as they tend to alter image color structure and distribution, the researchers are always finding alternative strategies for color quantization so that they may be prepared to select the most appropriate technique for the color quantization. The objective of this research work, is to implement a new algorithm for Color Image Quantization based on Bacteria Foraging Optimization. To compare the designed algorithm with other swarm intelligence techniques and to validate the proposed work. The proposed algorithm is then applied to commonly used images including the phantom images. The conducted experiments indicate that proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches.