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

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.

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Color Quantization Based on Gaussian Mixture Mode

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.457-458.650 ◽

2012 ◽

Vol 457-458 ◽

pp. 650-654

Author(s):

Qiu Chun Jin ◽

Xiao Li Tong

Keyword(s):

Image Analysis ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Color Image ◽

Gaussian Mixture ◽

Experimental Results ◽

Color Quantization ◽

Quantization Method ◽

Color Palette ◽

Image Quantization

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

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An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps

Computational Intelligence and Neuroscience ◽

10.1155/2016/5302957 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Hyun Jun Park ◽

Kwang Baek Kim ◽

Eui-Young Cha

Keyword(s):

Image Processing ◽

Processing Time ◽

Color Image ◽

Color Image Processing ◽

Self Organizing Map ◽

Original Image ◽

Color Quantization ◽

Quantization Method ◽

Self Organizing Maps ◽

Self Organizing

Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method.

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