Block-based histogram packing of color-quantized images
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Journal on Image and Video Processing - Color in Image and Video Processing
Unsupervised Video Shot Segmentation Using Global Color and Texture Information
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Color quantization by 3D spherical Fibonacci lattices
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Improving the performance of k-means for color quantization
Image and Vision Computing
Using perceptual color contrast for color image processing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
A color image segmentation algorithm by using region and edge information
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Combined color and texture segmentation based on fibonacci lattice sampling and mean shift
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
An efficient color quantization based on generic roughness measure
Pattern Recognition
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Color quantization is sampling of three-dimensional (3-D) color spaces (such as RGB or Lab) which results in a discrete subset of colors known as a color codebook or palette. It is extensively used for display, transfer, and storage of natural images in Internet-based applications, computer graphics, and animation. We propose a sampling scheme which provides a uniform quantization of the Lab space. The idea is based on several results from number theory and phyllotaxy. The sampling algorithm is very much systematic and allows easy design of universal (image-independent) color codebooks for a given set of parameters. The codebook structure allows fast quantization and ordered dither of color images. The display quality of images quantized by the proposed color codebooks is comparable with that of image-dependent quantizers. Most importantly, the quantized images are more amenable to the type of processing used for grayscale ones. Methods for processing grayscale images cannot be simply extended to color images because they rely on the fact that each gray-level is described by a single number and the fact that a relation of full order can be easily established on the set of those numbers. Color spaces (such as RGB or Lab) are, on the other hand, 3-D. The proposed color quantization, i.e., color space sampling and numbering of sampled points, makes methods for processing grayscale images extendible to color images. We illustrate possible processing of color images by first introducing the basic average and difference operations and then implementing edge detection and compression of color quantized images