Selection of small color palette for color image quantization

1992 ◽  
Author(s):  
Wing K. Chau ◽  
S. K. M. Wong ◽  
Xuedong Yang ◽  
Shijie J. Wan
Keyword(s):  
Color Image ◽  
Color Palette ◽  
Image Quantization ◽  
Selection Of

Color Quantization Based on Gaussian Mixture Mode

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.

scholarly journals Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1222
Author(s):  
Shu-Chien Huang
Keyword(s):  
Artificial Bee Colony ◽  
Color Image ◽  
Search Algorithm ◽  
Computation Time ◽  
Color Palette ◽  
Bee Colony ◽  
Image Quantization ◽  
Important Approach ◽  
Three Stages ◽  
Color Pixel

Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.

scholarly journals An Efficient Palette Generation Method for Color Image Quantization

2021 ◽  
Vol 11 (3) ◽  
pp. 1043
Author(s):  
Shu-Chien Huang
Keyword(s):  
Color Image ◽  
Sampling Rate ◽  
Color Space ◽  
Computation Time ◽  
Input Image ◽  
Blue Color ◽  
Color Palette ◽  
Image Quantization ◽  
Searching Method ◽  
Two Stages

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

Color image approximation based on clustering a color palette and smoothing borders with splines and arcs

2018 ◽  
pp. 3-11
Author(s):  
D. Kasimov ◽  
◽  
A. Kuchuganov ◽  
V. Kuchuganov ◽  
P. Oskolkov ◽  
...  
Keyword(s):  
Color Image ◽  
Color Palette ◽  
Image Approximation

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

Joint index coding and reversible data hiding methods for color image quantization

2019 ◽  
Vol 78 (24) ◽  
pp. 35537-35558 ◽  
Author(s):  
Jun-Chou Chuang ◽  
Yu-Chen Hu ◽  
Chia-Mei Chen ◽  
Yu-Hsiu Lin ◽  
Yu Chen
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Color Image ◽  
Index Coding ◽  
Image Quantization
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