The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
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When using the probability matrix to pick from the candidate set, it is important that the candidate array be sorted in advance. Not doing so will fail to preserve the patterns distinctive of ordered dithering. A good approach is to sort the candidate colours by luminance, or the measure of a colour’s lightness4. When this is done, we effectively minimise the contrast between successive candidates in the array, making it easier to observe the pattern embedded the matrix.