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.
* 时间复杂度: O(n²) 最好: O(n) 空间复杂度: O(1) 稳定: ✓,推荐阅读服务器推荐获取更多信息
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LiteRT-LM 包 — 使用 ai-edge-torch-nightly 转换为 .litertlm 文件,并添加元数据和停止标记,用于 LiteRT-LM 运行时
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