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Another way to look at our threshold matrix is as a kind of probability matrix. Instead of offsetting the input pixel by the value given in the threshold matrix, we can instead use the value to sample from the cumulative probability of possible candidate colours, where each colour is assigned a probability or weight . Each colour’s weight represents it’s proportional contribution to the input colour. Colours with greater weight are then more likely to be picked for a given pixel and vice-versa, such that the local average for a given region should converge to that of the original input value. We can call this the N-candidate approach to palette dithering.，详情可参考51吃瓜
The future of organic visibility includes AI citations alongside traditional search rankings. The question isn't whether to optimize for both—it's whether you'll start while competition is light or wait until fighting for AI visibility becomes as challenging as ranking in traditional search is today.