Nitter complete color theory

M10. Computational Palette and Image Analysis

Five connected studies move computational palette and image analysis from direct observation and explanation through controlled experiment, application and critique. Five active studies connect evidence, experiment, application and critique.

Observe → Understand → Experiment → Apply → Critique and record

Open any study. Review state, labs, reflections and artifacts stay local to this browser.

• Sampling

  Begin Computational Palette and Image Analysis with sampling: observe directly and record the conditions before interpreting the result.

• Clustering

  Within Computational Palette and Image Analysis, build a working explanation of clustering, keeping the model distinct from the appearance it describes.

• Dominant-Color Bias

  In Computational Palette and Image Analysis, test dominant-color bias by changing one controlled variable and comparing the evidence.

• Palette Scoring

  Apply palette scoring within Computational Palette and Image Analysis to a practical color decision, preserving both the reasoning and its constraints.

• Extraction, Dithering and Quantization

  In Computational Palette and Image Analysis, critique extraction, dithering and quantization against evidence, exceptions and failure conditions before carrying it forward.