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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.
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.
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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When scaling a vector graphic image, the graphic primitives that make up the image can be scaled using geometric transformations with no loss of image quality. When scaling a raster graphics image, a new image with a higher or lower number of pixels must be generated. In the case of decreasing the pixel number (scaling down), this usually ...
While Facebook and Prisma tap AI to transform everyday images and video into flowing artworks, NVIDIA is aiming for all-out realism. The graphics card-maker just released a paper detailing its use ...
Ad blocking reduces page load time and saves bandwidth for the users. Users who pay for total transferred bandwidth ("capped" or pay-for-usage connections) including most mobile users worldwide have a direct financial benefit from filtering an ad before it is loaded. Using an ad blocker is a common method of improving internet speeds. [39]
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