Color quantization is primarily used in computer graphics research literature; in applications, terms such as optimized palette generation, optimal palette generation, or decreasing color depth are used. Some of these are misleading, as the palettes generated by standard algorithms are not necessarily the best possible. - wikipedia

• How did the need for color quantization arise? - from Leptonica, good overview & source of implementation.

Fast color quantization in OpenCV

• nQuantGpp - nQuantGpp includes top 10 color quantization algorithms for g++ producing high quality optimized images. nQuantGpp is ported from nQuantCpp which migrates to OpenCV to leverage the deep learning features of such popular library.
  • nQuantCpp - includes top 6 color quantization algorithms f - Linux #7 - it once compiled on Linux, but currently it does not because it depends on GdiPlus.
• OctreeColorQuantizer-opencv - Color quantization of images using octrees (implemented in C++ with OpenCV)
• Forty years of color quantization: a modern, algorithmic survey
  • C++ – Fast color quantization in OpenCV

Color quantization using k-means

This iterate several times over the whole images before convergence.

• ChatGPT code

Median Cut

Reduce processing on each iteration.

Own Implementation

• Color Quantisation - Color Quantisation CLI

see also

• How does color segmentation differ from color quantization?