Digital halftoning
Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
An analysis of selected computer interchange color spaces
ACM Transactions on Graphics (TOG)
Color Image Quantization By Agglomerative Clustering
IEEE Computer Graphics and Applications
Center-cut for color-image quantization
The Visual Computer: International Journal of Computer Graphics
P-Complete Approximation Problems
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
A VLSI architecture for 3-D self-organizing map based color quantization and its FPGA implementation
Journal of Systems Architecture: the EUROMICRO Journal
An overview of clustering methods
Intelligent Data Analysis
The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas
Expert Systems with Applications: An International Journal
Fusion of multi-exposure images
Image and Vision Computing
Improving the performance of k-means for color quantization
Image and Vision Computing
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Batch neural gas with deterministic initialization for color quantization
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
An efficient color quantization based on generic roughness measure
Pattern Recognition
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One of the numerical criteria for color image quantization is to minimize the maximum discrepancy between original pixel colors and the corresponding quantized colors. This is typically carried out by first grouping color points into tight clusters and then finding a representative for each cluster. In this article we show that getting the smallest clusters under a formal notion of minimizing the maximum intercluster distance does not guarantee an optimal solution for the quantization criterion. Nevertheless our use of an efficient clustering algorithm by Teofilo F. Gonzalez, which is optimal with respect to the approximation bound of the clustering problem, has resulted in a fast and effective quantizer. This new quantizer is highly competitive and excels when quantization errors need to be well capped and when the performance of other quantizers may be hindered by such factors as low number of quantized colors or unfavorable pixel population distribution. Both computer-synthesized and photographic images are used in experimental comparison with several existing quantization methods.