Another way to approach dithering is to analyse the input image in order to make informed decisions about how best to perturb pixel values prior to quantisation. Error-diffusion dithering does this by sequentially taking the quantisation error for the current pixel (the difference between the input value and the quantised value) and distributing it to surrounding pixels in variable proportions according to a diffusion kernel . The result is that input pixel values are perturbed just enough to compensate for the error introduced by previous pixels.
复旦大学老龄研究院教授申琦将这种现象称为:老年人大模型使用中的“提问沟”。,推荐阅读旺商聊官方下载获取更多信息
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