quantizer

func QuantizeCelebi ¶

func QuantizeCelebi(input []color.ARGB, maxColor int) QuantizedMap

QuantizeCelebi is an image quantizer that improves on the quality of a standard K-Means algorithm by setting the K-Means initial state to the output of a Wu quantizer, instead of random centroids. Improves on speed by several optimizations, as implemented in Wsmeans, or Weighted Square Means, K-Means with those optimizations.

This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395

func QuantizeCelebiContext ¶

func QuantizeCelebiContext(
	ctx context.Context,
	input []color.ARGB,
	maxColor int,
) (QuantizedMap, error)

QuantizeCelebiContext is QuantizeCelebi with context.Context support. Returns ctx.Err() if context is Done.

func QuantizeMap ¶

func QuantizeMap(input []color.ARGB) QuantizedMap

QuantizeMap takes a slice of []color.Color and returns Quantized

func QuantizeWsMeans ¶

func QuantizeWsMeans(
	input []color.ARGB,
	startingClusters []color.Lab,
	maxColors int,
) QuantizedMap

QuantizeWsMeans is an image quantizer that improves on the speed of a standard K-Means algorithm by implementing several optimizations, including deduping identical pixels and a triangle inequality rule that reduces the number of comparisons needed to identify which cluster a point should be moved to.

Wsmeans stands for Weighted Square Means.

This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395

func QuantizeWsMeansContext ¶

func QuantizeWsMeansContext(
	ctx context.Context,
	input []color.ARGB,
	startingClusters []color.Lab,
	maxColors int,
) (QuantizedMap, error)

QuantizeWsMeansContext is QuantizeWsMeans with context.Context support.