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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Instead of taking the nearest candidates to , we can look for a set of candidates whose centroid is close to . The N-convex algorithm works by finding the closest colour to a given target colour for iterations, where the target is first initialised to be equal to the input pixel. Every iteration the closest colour added to the candidate list, and the quantisation error between it and the original input pixel is added to the target.