Мерц резко сменил риторику во время встречи в Китае09:25
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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.
It completed the assignment in one-shot, accounting for all of the many feature constraints specified. The “Python Jupyter Notebook” notebook command at the end is how I manually tested whether the pyo3 bridge worked, and it indeed worked like a charm. There was one mistake that’s my fault however: I naively chose the fontdue Rust crate as the renderer because I remember seeing a benchmark showing it was the fastest at text rendering. However, testing large icon generation exposed a flaw: fontdue achieves its speed by only partially rendering curves, which is a very big problem for icons, so I followed up: