Dithering noise, i.e. the difference between the original signal and the dithered signal, needs to be considered in "frequency space" like any other kind of noise.
Dithering noise has the special feature of depending very strongly on the sampling rate and (statistically) very weakly on the input signal being dithered.
Dithering allows replacing high-accuracy sampling (typically constrained by computer word lengths) with high-sample rate sampling (typically cheap): without using more accurate arithmetic, analog and often completely free lowpass filtering (such as looking at a dithered image from far enough) makes N samples, with M bits of accuracy each, equivalent to one sample with M+log N bits of accuracy.
Dithering noise has the special feature of depending very strongly on the sampling rate and (statistically) very weakly on the input signal being dithered.
Dithering allows replacing high-accuracy sampling (typically constrained by computer word lengths) with high-sample rate sampling (typically cheap): without using more accurate arithmetic, analog and often completely free lowpass filtering (such as looking at a dithered image from far enough) makes N samples, with M bits of accuracy each, equivalent to one sample with M+log N bits of accuracy.