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.
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The latest foreboding is from Citrini Research, a little-known US firm that provides insights on “transformative ‘megatrends’”. Its post on Substack, which it called a “scenario, not a prediction”, rattled investors by portraying a near future in which autonomous AI systems – or agents – upend the entire US economy, from jobs to markets and mortgages.
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新建大型数据中心PUE必须低于1.25,国家枢纽节点低于1.21。液冷、余热回收、智能节能强制普及,单位算力能耗持续下降,形成“降碳=降本”的正向循环。
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