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What is Color Quantization?

Color quantization maps an image’s source colors to a smaller representative palette. The process reduces the number of distinct colors while attempting to limit visible differences from the original.

How Color Quantization works

A quantizer first chooses representative colors, then assigns each source pixel to one of those palette entries under a distance rule. Palette construction may use histograms, clustering, median-cut-style partitioning, or other optimization methods, and mapping may be direct or dithered. This differs from reducing numeric bit depth independently in each channel because the palette can represent irregularly distributed colors. It usually occurs late in image export, after resizing and color conversion.

Key facts

  1. Palette selection and pixel assignment are separate problems: a good set of representative colors can still look poor if the distance metric does not reflect visible color differences.
  2. Error-diffusion dithering passes quantization error to neighboring pixels, reducing large smooth bands but making results dependent on scan order and less locally compressible.
  3. Quantizing before resizing can contaminate interpolation with palette constraints; resizing a higher-precision image first generally gives the quantizer better source samples.

When Color Quantization matters

Apply quantization to meet indexed-format limits, reduce file size, or extract dominant colors. Too small a palette can introduce banding, while dithering can hide bands at the cost of added visual noise.

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