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A new approach, area-based anti-aliasing (ABAA), relies on subpixel area sampling. It is the fastest and produces the best static and moving images with anti-aliasing. Currently, there is no readily available product using
In computer graphics, anti-aliasing improves the appearance of "jagged" polygon edges, or "jaggies", so they 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).
Multisample anti-aliasing (MSAA) is a type of spatial anti-aliasing, a technique used in computer graphics to remove jaggies. It is an optimization of supersampling, where only the necessary parts are sampled more. Jaggies are only noticed in a small area, so the area is quickly found, and only that is anti-aliased.
Supersampling or supersampling anti-aliasing (SSAA) is a spatial anti-aliasing method, i.e. a method used to remove aliasing (jagged and pixelated edges, colloquially known as "jaggies") from images rendered in computer games or other computer programs that generate imagery. Aliasing occurs because unlike real-world objects, which have ...
Temporal anti-aliasing (TAA) is a spatial anti-aliasing technique for computer-generated video that combines information from past frames and the current frame to remove jaggies in the current frame. In TAA, each pixel is sampled once per frame but in each frame the sample is at a different location within the frame.
Deep learning anti-aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. [1] DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. [1]DLAA is similar to deep learning super sampling (DLSS) in its anti-aliasing method, [2] with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, [3] whereas the ...
Anti-aliasing is achieved by blending pixels in these borders, according to the pattern they belong to and their position within the pattern. [ 1 ] [ 2 ] [ 3 ] Enhanced subpixel morphological antialiasing, or SMAA, is an image-based GPU-based implementation of MLAA [ 4 ] developed by Universidad de Zaragoza and Crytek .
The main advantage of this technique over conventional spatial anti-aliasing is that it does not require large amounts of computing power.It achieves this by smoothing undesirable jagged edges ("jaggies") [4] as pixels, according to how they appear on-screen, rather than analyzing the 3D model itself, as in conventional spatial anti-aliasing. [1]