The tee() memory cliff: Stream.share() requires explicit buffer configuration. You choose the highWaterMark and backpressure policy upfront — no more silent unbounded growth when consumers run at different speeds.
Knowing this, we can modify the N-Convex algorithm covered earlier such that the candidate weights are given by the barycentric coordinates of the input pixel after being projected onto a triangle whose vertices are given by three surrounding colours, abandoning the IDW method altogether1. This results in a fast and exact minimisation of , with the final dither being closer in quality to that of Knoll’s Algorithm.
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