Sampling theoremIn order to accurately reconstruct a signal from a periodically sampled version of it, the sampling frequency must be at least twice the maximum frequency of the signal. (Nyquist’s theorem)

Due to limited resolution in the spatial (pixel-size) or colour domain, we are forced to under sample. This results in effects called aliasing.

Anti-aliasingTo reduce the effect of aliasing various anti-aliasing techniques can be applied. Some examples are:

•Low-pass filtering (poor mans anti-aliasing)•Area-sampling (colour proportional to area)•Super-sampling (more samples than pixels)•Dithering (anti-aliasing in the colour domain)

Area-samplingIntensity proportional to the area covered by the object.

Simplified version: Intensity proportional to (1-distance to pixel)

Super-samplingNormal rasterisation, in a frame-buffer of higher resolution than the screen. Average the intensity of the covered sub-pixels when setting the real pixel value.

6/9 green + 3/9 white

Anti-aliasing solves the problem of sliveras well as the problem of shared vertices.

Sliver