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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 ...
The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
The input data is the rendered image and optionally the luminance data. [3]Acquire the luminance data. [3] This data could be passed into the FXAA algorithm from the rendering step as an alpha channel embedded into the image to be antialiased, calculated from the rendered image, or approximated by using the green channel as the luminance data.
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In today's video, I discuss recent updates affecting Nvidia (NASDAQ: NVDA), Amazon (NASDAQ: AMZN), Marvell (NASDAQ: MRVL), and other semiconductor updates. To learn more, check out the short video ...
When the lower level aliasing is suppressed, to make the third image and then that is down-sampled once more, without anti-aliasing, to make the fifth image, the order on the scale of the third image appears as systematic aliasing in the fifth image. Pure down-sampling of an image has the following effect (viewing at full-scale is recommended):
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Nvidia advertised DLSS as a key feature of the GeForce 20 series cards when they launched in September 2018. [5] At that time, the results were limited to a few video games, namely Battlefield V, [6] or Metro Exodus, because the algorithm had to be trained specifically on each game on which it was applied and the results were usually not as good as simple resolution upscaling.