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'A dangerous precedent'
Transforms don't execute until the consumer pulls. There's no eager evaluation, no hidden buffering. Data flows on-demand from source, through transforms, to the consumer. If you stop iterating, processing stops.
The N-closest or N-best dithering algorithm is a straightforward solution to the N-candidate problem. As the name suggests, the set of candidates is given by the closest palette colours to the input pixel. To determine their weights, we simply take the inverse of the distance to the input pixel. This is essentially the inverse distance weighting (IDW) method for multivariate interpolation, also known as Shepard’s method. The following pseudocode sketches out a possible implementation: