Spatial anti-aliasing

In digital signal processing, spatial anti-aliasing is the technique of minimising the distortion artifacts known as aliasing when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications.

Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as the black-and-white noise near the top of figure 1-a below.

In signal acquisition and audio, anti-aliasing is often done using an analogue anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analogue-to-digital converter. In digital photography, optical anti-aliasing filters are made of birefringent materials, and smooth the signal in the spatial optical domain. The anti-aliasing filter essentially blurs the image slightly in order to reduce the resolution to or below that achievable by the digital sensor (the larger the pixel pitch, the lower the achievable resolution at the sensor level).

In computer graphics, anti-aliasing improves the appearance of polygon edges, so they are not "jagged" but are smoothed out on the screen. However, it incurs a performance cost for the graphics card and uses more video memory. The level of anti-aliasing determines how smooth polygon edges are (and how much video memory it consumes).

Figure 1-a illustrates the visual distortion that occurs when anti-aliasing is not used. Near the top of the image, where the checker-board is very small, the image is both difficult to recognise and not aesthetically appealing. In contrast, Figure 1-b shows an anti-aliased version of the scene. The checker-board near the top blends into grey, which is usually the desired effect when the resolution is insufficient to show the detail. Even near the bottom of the image, the edges appear much smoother in the anti-aliased image. Figure 1-c shows another anti-aliasing algorithm, based on the sinc filter, which is considered better than the algorithm used in 1-b.

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