Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
Color image quantization by minimizing the maximum intercluster distance
ACM Transactions on Graphics (TOG)
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
An adaptive clustering algorithm for color quantization
Pattern Recognition Letters
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Digital Color Imaging Handbook
Digital Color Imaging Handbook
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Colour quantization by three-dimensional frequency diffusion
Pattern Recognition Letters
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
An adjustable algorithm for color quantization
Pattern Recognition Letters
Some refinements of rough k-means clustering
Pattern Recognition
Color image segmentation: Rough-set theoretic approach
Pattern Recognition Letters
Rough Image Colour Quantisation
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Improving the performance of k-means for color quantization
Image and Vision Computing
IEEE Transactions on Signal Processing
Vector order statistics operators as color edge detectors
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information measures in scale-spaces
IEEE Transactions on Information Theory
On spatial quantization of color images
IEEE Transactions on Image Processing
Color quantization and processing by Fibonacci lattices
IEEE Transactions on Image Processing
Color edge detection using vector order statistics
IEEE Transactions on Image Processing
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
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Color quantization is a process to compress image color space while minimizing visual distortion. The quantization based on preclustering has low computational complexity but cannot guarantee quantization precision. The quantization based on postclustering can produce high quality quantization results. However, it has to traverse image pixels iteratively and suffers heavy computational burden. Its computational complexity was not reduced although the revised versions have improved the precision. In the work of color quantization, balancing quantization quality and quantization complexity is always a challenging point. In this paper, a two-stage quantization framework is proposed to achieve this balance. In the first stage, high-resolution color space is initially compressed to a condensed color space by thresholding roughness indices. Instead of linear compression, we propose generic roughness measure to generate the delicate segmentation of image color. In this way, it causes less distortion to the image. In the second stage, the initially compressed colors are further clustered to a palette using Weighted Rough K-means to obtain final quantization results. Our objective is to design a postclustering quantization strategy at the color space level rather than the pixel level. Applying the quantization in the precisely compressed color space, the computational cost is greatly reduced; meanwhile, the quantization quality is maintained. The substantial experimental results validate the high efficiency of the proposed quantization method, which produces high quality color quantization while possessing low computational complexity.