Palette quantization reduces colors without making images muddy

Short summary

Palette quantization reduces colors without losing readability by treating it as a structure problem, not just color matching; simple nearest-color algorithms fail because they optimize locally. Use perceptual color spaces like Lab/OKLab and differentiated region treatment—preserve important details like faces while simplifying backgrounds—then show users previews before they commit to a palette size to balance realism, readability, and editability tradeoffs.

• •Palette quantization is fundamentally a structure problem requiring global image awareness, not just local color accuracy
• •Perceptual color spaces (Lab/OKLab) match human vision better than RGB for distance metrics
• •Different image regions deserve different color budgets: preserve contrast in faces/edges, simplify backgrounds
• •Preview before commit: palette size represents user tradeoffs between realism, smoothness, and editability
• •Post-quantization cleanup of isolated pixels improves perceived intention and readability

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