the number of ways in which this can be done is infinite, there are a few ways which are commonly used. Grid The simplest algorithm. The pixel is split into several sub-pixels, and a sample is taken from the center of each. It is fast and easy to implement. Although, due to the regular nature of sampling, aliasing can still occur if a low number of sub-pixels is used. Random Also known as stochastic sampling, it avoids the regularity of grid supersampling. However, due to the irregularity of the pattern, samples end up being unnecessary in some areas of the pixel and lacking in others. Poisson disk The Poisson disk sampling algorithm places the samples randomly, but then checks that any two are not too close. The end result is an even but random distribution of samples. The naive "dart throwing" algorithm is extremely slow for large data sets, which once limited its applications for real-time rendering. However, many fast algorithms now exist to generate Poisson disk noise, even those with variable density. Jittered A modification of the grid algorithm to approximate the Poisson disk. A pixel is split into several sub-pixels, but a sample is not taken from the center of each, but from a random point within the sub-pixel. Congregation can still occur, but to a lesser degree. Rotated grid A 2×2 grid layout is used but the sample pattern is rotated to avoid samples aligning on the horizontal or vertical axis, greatly improving antialiasing quality for the most commonly encountered cases. For an optimal pattern, the rotation angle is (about 26.6°) and the square is stretched by a factor of , making it also a 4-queens solution. See also Oversampling Multisample anti-aliasing Quincunx Graphics card Graphics processing unit Ray tracing (graphics) Framebuffer Game engine Image scaling 2×SaI Deep Learning Super Sampling References External links Image processing Anti-aliasing algorithms pl:Antyaliasing#Supersampling