An algorithm for multidimensional data clustering
ACM Transactions on Mathematical Software (TOMS)
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough array processing via fast multi-scale clustering
Real-Time Imaging
Gray-level reduction using local spatial features
Computer Vision and Image Understanding
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An adjustable algorithm for color quantization
Pattern Recognition Letters
IEEE Transactions on Signal Processing
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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
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Color quantization is the process of reducing the number of colors in an image. That is, color quantization maps a large number of colors into a much smaller number of representative colors while keeping color distortion to an acceptable level. The reduction in the number of colors lowers computational complexity associated with color processing, and achieves higher color image compression for storage and transmission purposes. The existing color quantization methods require that the number of representative or prominent colors be specified by the user. This paper presents a scene-adaptive color quantization method which eases this constraint by determining the number of representative colors automatically. This method utilizes the discrete wavelet transform to achieve a computationally efficient implementation of the multi-scale clustering algorithm in a 3D color space. The performance is evaluated in terms of compression ratio or number of representative colors, color distortion, and computational complexity. It is shown that the developed method outperforms the popular color quantization methods in terms of color distortion.