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One by-product of weighing the candidates by their distance is that the resulting output image is prone to false contours or banding. Increasing reduces this effect at the cost of added granularity or high frequency noise due to the introduction of ever more distant colours to the set. I recommend taking a look at the original paper if you’re interested in learning a bit more about the algorithm[1].
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You can include multimodal data like images. There’s something strange about including images when going back to Roman times or 1700 because while they had texts, they didn’t have digital images. However, this is acceptable for some purposes. You’d want to avoid leaking information that could only be known in the present. You could include things people at the time could see and experience themselves. For example, there may be no anatomically accurate painting in Roman times of a bee or an egg cracking, but you can include such images because people could see such things, even if they weren’t part of their recorded media. You could also have pictures of buildings and artifacts that we still have from the past.