Aliasing occurs because real-world objects have continuous, smooth curves and lines, whereas monitors can only display discrete points of light called pixels. Since pixels are uniformly colored and always of the same shape, lines become jagged.
Supersampling is an anti-aliasing technique, the process of eliminating jagged and pixelated edges (aliasing). It is a method of smoothing images rendered in computer games or other programs that generate imagery. Supersampling is one of the ways of solving this problem. Samples are taken at several instances inside the pixel (not just at the center as default) and an average color value is calculated. This is achieved by rendering the image at a much higher resolution than the one being displayed, then shrinking it to the desired size, using the extra pixels for calculation. The result is a downsampled image with smoother transitions from one line of pixels to another along the edges of objects.
The number of samples determines the quality of the output.
Computational cost and adaptive supersampling 
Supersampling is computationally expensive because it requires much greater video card memory and memory bandwidth, since the amount of buffer used is several times larger. A way around this problem is adaptive supersampling. This works by acknowledging that very few pixels will actually be on a boundary, therefore only these need to be supersampled.
At first only a few samples are made within a pixel. If these values are very similar, only these samples are used for determining color. If not, more are used. The result of this method is that a higher number of samples are calculated only where necessary, thus improving performance.
Supersampling patterns 
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When taking samples inside of a pixel the sample positions have to be determined in some way. Although the number of ways in which this can be done are infinite, there are a few different ways that are among the most common.
The simplest algorithm. The pixel is split in 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.
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 disc 
Again an algorithm that places the samples randomly, but then checks that any two are not too close. The end result is even but random distribution of samples. Unfortunately, the computational time required for this algorithm is too great to justify its use in real-time rendering, unless the sampling itself is computationally expensive compared to the positioning the sample points or the sample points are not repositioned for every single pixel.
A modification of the grid algorithm to approximate the Poisson disc. 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 arctan(1/2) (about 26.6 degrees) and the square is stretched by a factor of √/2.
See also 
- Multisample anti-aliasing
- Graphics card
- Graphics processing unit
- Ray tracing (graphics)
- Game engine
- Image scaling
- "High-Resolution Antialiasing (HRAA)". Technical Brief: High-Resolution Antialiasing through Multisampling. Retrieved May 2006.
- "What is supersampling (antialiasing technique)?". Hardware Knowledgebase. Archived from the original on 2006-03-25. Retrieved May 2006.
- "Supersampling". Everything2. Retrieved May 2006.
- "Image Interpolation with Contour Stencils". Image Processing On Line. Retrieved October 2010.