Center-cut for color-image quantization
The Visual Computer: International Journal of Computer Graphics
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On Spatial Quantization of Color Images
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Colour quantization by three-dimensional frequency diffusion
Pattern Recognition Letters
Adaptive color quantization based on perceptive edge protection
Pattern Recognition Letters
Color quantization of compressed video sequences
IEEE Transactions on Circuits and Systems for Video Technology
DWT-based scene-adaptive color quantization
Real-Time Imaging - Special issue on multi-dimensional image processing
Region partition and feature matching based color recognition of tongue image
Pattern Recognition Letters
Color reduction based on ant colony
Pattern Recognition Letters
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Improving the performance of k-means for color quantization
Image and Vision Computing
Multiresolution histogram analysis for color reduction
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
An ant-based approach to color reduction
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
An efficient color quantization based on generic roughness measure
Pattern Recognition
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Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization.