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I'm not sure I follow. To me dithering comes from quantization. I don't think you should think of dithering in frequency space. https://bjango.com/images/artic

I'm not sure I follow. To me dithering comes from quantization. I don't think you should think of dithering in frequency space. https://bjango.com/images/articles/gradients/dithering-extreme.png https://bjango.com/images/articles/gradients/dithering-extre... Illustrates the same signal dithered or not. No sampling rate involved. And please correct me if i'm wrong, but you can not trade sample accuracy for sampling rate.

gugagore 9y ago
You can trade sample accuracy for sample rate. This is how I directly interpret ordered dithering. The following line on the wikipedia article communicates (maybe) this: "The size of the map selected should be equal to or larger than the ratio of source colors to target colors. For example, when quantizing a 24bpp image to 15bpp (256 colors per channel to 32 colors per channel), the smallest map one would choose would be 4x2, for the ratio of 8 (256:32). This allows expressing each distinct tone of the input with different dithering patterns." Same idea applies to 1D signals (from https://www.wikiwand.com/en/Delta-sigma_modulation https://www.wikiwand.com/en/Delta-sigma_modulation : " used to convert high bit-count, low-frequency digital signals into lower bit-count, higher-frequency digital signals") same idea: https://www.wikiwand.com/en/Class-D_amplifier https://www.wikiwand.com/en/Class-D_amplifier https://www.wikiwand.com/en/Pulse-density_modulation https://www.wikiwand.com/en/Pulse-density_modulation
HelloNurse 9y ago
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.
dreamcompiler 9y ago
You can indeed trade sample accuracy for sample rate as long as information is not lost -- and in dithering, information is preserved by diffusing the quantization error spatially. Look at the image you posted; on the right, the effective "pixels" have become larger while each has acquired more gray levels. That's a lower sample frequency traded for higher quantization resolution.

gugagore 9y ago

You can trade sample accuracy for sample rate. This is how I directly interpret ordered dithering. The following line on the wikipedia article communicates (maybe) this: "The size of the map selected should be equal to or larger than the ratio of source colors to target colors. For example, when quantizing a 24bpp image to 15bpp (256 colors per channel to 32 colors per channel), the smallest map one would choose would be 4x2, for the ratio of 8 (256:32). This allows expressing each distinct tone of the input with different dithering patterns." Same idea applies to 1D signals (from https://www.wikiwand.com/en/Delta-sigma_modulation https://www.wikiwand.com/en/Delta-sigma_modulation : " used to convert high bit-count, low-frequency digital signals into lower bit-count, higher-frequency digital signals") same idea: https://www.wikiwand.com/en/Class-D_amplifier https://www.wikiwand.com/en/Class-D_amplifier https://www.wikiwand.com/en/Pulse-density_modulation https://www.wikiwand.com/en/Pulse-density_modulation

HelloNurse 9y ago

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.

dreamcompiler 9y ago

You can indeed trade sample accuracy for sample rate as long as information is not lost -- and in dithering, information is preserved by diffusing the quantization error spatially. Look at the image you posted; on the right, the effective "pixels" have become larger while each has acquired more gray levels. That's a lower sample frequency traded for higher quantization resolution.