Investigation on Color Quantization Algorithm of Color Image

2011 ◽  
pp. 181-187 ◽  
Author(s):  
Yueqiu Jiang ◽  
Yang Wang ◽  
Lei Jin ◽  
Hongwei Gao ◽  
Kunlei Zhang
Keyword(s):  
Color Image ◽  
Color Quantization

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

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 963
Author(s):  
Mariusz Frackiewicz ◽  
Aron Mandrella ◽  
Henryk Palus
Keyword(s):  
Nearest Neighbor ◽  
Color Image ◽  
Assessment Process ◽  
Color Image Processing ◽  
Color Quantization ◽  
Quantization Method ◽  
Quality Indices ◽  
Limited Sample ◽  
High Quality ◽  
Image Quantization

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.

scholarly journals Pembentua Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion

2021 ◽  
Vol 8 (3) ◽  
pp. 625
Author(s):  
Syahrial Syahrial ◽  
Rizal Lamusu
Keyword(s):  
Design Pattern ◽  
Color Image ◽  
Threshold Value ◽  
Forest Edge ◽  
Gaussian Filter ◽  
Color Quantization ◽  
Edge Image ◽  
Pattern Design ◽  
Testing Parameter ◽  
Parameter Values

<p class="Abstrak">Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya. Gambar yang diterapkan pada pola memiliki resolusi sangat rendah dan harus mempertahankan bentuknya. Penelitian ini mengembangkan metode pembentukan pola desain motif Karawo dari citra digital. Proses dilakukan dengan pengolahan awal menggunakan <em>k-means color quantization (KMCQ)</em> dan deteksi tepi <em>structured forest</em>. Proses selanjutnya melakukan pengurangan resolusi menggunakan metode <em>pixelation</em> dan <em>binarization</em>. Luaran dari algoritma menghasilkan 3 citra berbeda dengan ukuran yang sama, yaitu: citra tepi, citra biner, dan citra berwarna. Ketiga citra tersebut selanjutnya dilakukan proses pembentukan pola desain motif Karawo dengan berbagai petunjuk pola bagi pengrajin. Hasil menunjukkan bahwa pola desain motif dapat digunakan dan dimengerti oleh para pengrajin dalam menerapkannya di sulaman Karawo. Pengujian nilai-nilai parameter dilakukan pada metode <em>k-means</em>, <em>gaussian filter</em>, <em>pixelation</em>, dan <em>binarization.</em> Parameter-parameter tersebut yaitu: k pada <em>k-means</em>, <em>kernel</em> pada <em>gaussian filter</em>, lebar piksel pada <em>pixelation</em>, dan nilai <em>threshold</em> pada <em>binarization</em>. Pengujian menunjukkan nilai terendah tiap parameter adalah k=4, kernel=3x3, lebar piksel=70, dan <em>threshold</em>=20. Hasil memperlihatkan makin tinggi nilai-nilai tersebut maka semakin baik pola desain motif yang dihasilkan. Nilai-nilai tersebut merupakan nilai parameter terendah dalam pembentukan pola desain motif berkualitas baik berdasarkan indikator-indikator dari desainer.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Karawo embroidery is a unique handicraft from Gorontalo. The embroidery motif is applied in detail based on a certain design pattern. These patterns are depicted on paper with various guides. The image applied to the pattern is very low resolution and retains its shape. This study develops a method to generate a Karawo design pattern from a digital image. The process begins by using k-means color quantization (KMCQ) to reduce the number of colors and edge detection of the structured forest. The next process is to change the resolution using pixelation and binarization methods. The output algorithm produces 3 different state images of the same size, which are: edge image, binary image, and color image. These images are used in the formation of the Karawo motif design pattern. The motif contains various pattern instructions for the craftsman. The results show that it can be used and understood by the craftsmen in its application in Karawo embroidery. Testing parameter values on the k-means method, Gaussian filter, pixelation, and binarization. These parameters are k on KMCQ, the kernel on a gaussian filter, pixel width in pixelation, and threshold value in binarization. The results show that the lowest value of each parameter is k=4, kernel=3x3, pixel width=70, and threshold=20. The results show that the higher these values, the better the results of the pattern design motif. Those values are the lower input to generate a good quality pattern design based on the designer’s indicators.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

A color image watermarking scheme based on color quantization

2004 ◽  
Vol 84 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Piyu Tsai ◽  
Yu-Chen Hu ◽  
Chin-Chen Chang
Keyword(s):  
Color Image ◽  
Image Watermarking ◽  
Color Quantization ◽  
Color Image Watermarking ◽  
Watermarking Scheme

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

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
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.

GENERAL IMAGE CLASSIFICATION USING ADAPTIVE CELLULAR COLOR DECOMPOSITION

2003 ◽  
Vol 17 (08) ◽  
pp. 1383-1415 ◽  
Author(s):  
CHIH-HSIEN LO ◽  
SHU-YUAN CHEN
Keyword(s):  
Similarity Measure ◽  
Color Image ◽  
Color Space ◽  
Hierarchical Classification ◽  
Similarity Measures ◽  
Color Quantization ◽  
Query Image ◽  
Single Class ◽  
Quantization Step ◽  
Coarse To Fine

In this paper, a coarse-to-fine hierarchical classification method based on the features derived from adaptive cellular color decomposition is proposed. The proposed method is general and can be applied to all kinds of color image databases as long as a sample set of images have been classified. In addition, the number of classes can be as versatile as required. To achieve the goal mentioned above, our method consists of two phases: color quantization and classification. In the color quantization step, cellular decomposition is used to adaptively quantize color images in the HSV color space since H and S components construct a hexagon structure that is same as the cellular pattern. In the classification step, a coarse-to-fine strategy is employed. In the coarse stage, five image-based features extracted directly from the quantization results of the query images are used to prune irrelevant database images. In the fine stage, two cluster-based features are extracted from a small set of candidate images using closest-cluster matching. On the other hand, according to feature evaluation, one image-based and two cluster-based features are selected to derive individual-based similarity measure, which, in turn, is used to measure image-to-image similarity. In addition, class-based similarity measure using class characteristics is proposed to evaluate image-to-class similarity. Candidate images are then sorted according to the similarity measure, which is a combination of individual-based and class-based similarity measures. Finally, k-NN rule is used to assign the query image to a single class according to the sorting results. The effectiveness and practicability of the proposed method have been demonstrated by various experimental results.

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