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See:
Description
Interface Summary | |
RGBQuantizer | An interface for an RGB color quantizer. |
Class Summary | |
ArbitraryPaletteQuantizer | A color quantizer that maps an RGBImage
to any given palette.
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MedianCutContourRemoval | Performs the Median Cut color quantization algorithm in combination with a contour removal algorithm. |
MedianCutNode | An instance of this node class represents a cuboid part of the color cube representing the three-dimensional RGB color space. |
MedianCutQuantizer | Performs the Median Cut color quantization algorithm for a given list of colors. |
OctreeColorQuantizer | Performs the octree color quantization algorithm for a given RGB truecolor image. |
OctreeNode | A single node in an octree. |
PopularityQuantizer | Performs the popularity color quantization algorithm that maps an image to the colors occurring most frequently in the input image. |
RGBColor | Encapsulates a single color from RGB (red, green, blue) color space plus a frequency counter. |
RGBColorComparator | Compares two RGBColor objects. |
RGBColorList | Holds an array of RGBColor objects. |
UniformPaletteQuantizer | A color quantizer that maps to a palette which is equidistantly distributed in the RGB color cube. |
Classes to perform color image quantization, the reduction of the number of unique colors in an image. This is a lossy operation. Usually a number of colors in the destination image is specified by the user (e.g. 256), then the quantization algorithm creates a copy of the input image that has no more than that number of colors. The goal is to be as close to the original as possible. Depending on the actual number of colors specified and the image content this can lead to varying results.
Quantization is usually done to reduce the amount of data necessary to represent the image. The cost for this reduction is a loss of information.
See the dithering package for algorithms that improve the result of color image quantization algorithms.
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