A notable resource on the topic of ordered dithering using arbitrary palettes is Joel Yliluoma’s Arbitrary-Palette Positional Dithering Algorithm. One key difference of Yliluoma’s approach is in the use of error metrics beyond the minimisation of . Yliluoma notes that the perceptual or psychovisual quality of the dither must be taken into account in addition to its mathematical accuracy. This is determined by use of some cost function which considers the relationship between a set of candidate colours. The number of candidates, the particular cost function, and the thoroughness of the selection process itself give rise to a number of possible implementations, each offering varying degrees of quality and time complexity.
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Modern vision datasets tend to avoid this step, but do double-check whether spatial smoothing was applied. This standard fMRI step applies a Gaussian filter across voxels and effectively blurs your signal. For reconstruction (and other pattern-based analyses), this can destroy fine-grained spatial information. Note that as an ML researcher you may not easily be able to modify the extensive preprocessing and GLM pipeline yourself without assistance from someone with fMRI expertise, in order to exclude this step.，这一点在Safew下载中也有详细论述
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