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 main priority of DLAA is improving image quality at the cost of performance (irrelevant of resolution upscaling or downscaling). [4] DLAA is similar to temporal anti-aliasing (TAA) in that they are both spatial anti-aliasing solutions relying on past frame data. [3] [5] Compared to TAA, DLAA is substantially better when it comes to shimmering, flickering, and handling small meshes like wires. [6]
DLAA collects game rendering data including raw low-resolution input, motion vectors, depth buffers, and exposure information. This information feeds into a convolutional neural network that processes the image to reduce aliasing while preserving fine detail. [3]
The neural network architecture employs an auto-encoder design trained on high-quality reference images. The training dataset includes diverse scenarios focusing on challenging cases like sub-pixel details, high-contrast edges, and transparent surfaces. The network then processes frames in real-time. [3]
Unlike traditional anti-aliasing solutions that rely on manually written heuristics, such as TAA, DLAA uses its neural network to preserve fine details while eliminating unwanted visual artifacts. [3]
TAA is used in many modern video games and game engines; [7] however, all previous implementations have used some form of manually written heuristics to prevent temporal artifacts such as ghosting and flickering. One example of this is neighborhood clamping which forcefully prevents samples collected in previous frames from deviating too much compared to nearby pixels in newer frames. This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method. [8]
DLAA uses an auto-encoder convolutional neural network [9] trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above. Because of this, DLAA can generally resolve detail better than other TAA and TAAU implementations, while also removing most temporal artifacts.
While DLSS handles upscaling with a focus on performance, DLAA handles anti-aliasing with a focus on visual quality. DLAA runs at the given screen resolution with no upscaling or downscaling functionality provided by DLAA. [10]
DLSS and DLAA share the same AI-driven anti-aliasing method. [11] As such, DLAA functions like DLSS without the upscaling part. Both are made by Nvidia and require Tensor Cores. However, DLSS and DLAA cannot be enabled at the same time, only one can be selected depending on whether performance or image quality is prioritized.
TechPowerUp found that "[c]ompared to TAA and DLSS, DLAA is clearly producing the best image quality, especially at lower resolutions", arguing that, while "DLSS was already doing a better job than TAA at reconstructing small objects", "DLAA does an even better job". [12]
Jaggies are artifacts in raster images, most frequently from aliasing, which in turn is often caused by non-linear mixing effects producing high-frequency components, or missing or poor anti-aliasing filtering prior to sampling.
In digital signal processing, spatial anti-aliasing is a technique for minimizing the distortion artifacts (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.
The GeForce 3 series (NV20) is the third generation of Nvidia's GeForce line of graphics processing units (GPUs). Introduced in February 2001, it advanced the GeForce architecture by adding programmable pixel and vertex shaders, multisample anti-aliasing and improved the overall efficiency of the rendering process.
In computer graphics, mipmaps or pyramids are pre-calculated, optimized sequences of images, each of which is a progressively lower resolution representation of the previous. The height and width of each image, or level, in the mipmap is a factor of two smaller than the previous level. Mipmaps do not have to be square. They are intended to increase rendering speed and reduce aliasing artifacts. A high-resolution mipmap image is used for high-density samples, such as for objects close to the camera; lower-resolution images are used as the object appears farther away. This is a more efficient way of downscaling a texture than sampling all texels in the original texture that would contribute to a screen pixel; it is faster to take a constant number of samples from the appropriately downfiltered textures. Mipmaps are widely used in 3D computer games, flight simulators, other 3D imaging systems for texture filtering, and 2D and 3D GIS software. Their use is known as mipmapping. The letters MIP in the name are an acronym of the Latin phrase multum in parvo, meaning "much in little".
In 3D computer graphics, anisotropic filtering(AF) is a technique that improves the appearance of textures, especially on surfaces viewed at sharp angles. It helps make textures look sharper and more detailed by reducing blur and aliasing that can occur when surfaces are angled away from the viewer. Anisotropic filtering works by applying different amounts of filtering in different directions, unlike simpler methods like bilinear and trilinear filtering which filter equally in all directions.
Scalable Link Interface (SLI) is the brand name for a now discontinued multi-GPU technology developed by Nvidia for linking two or more video cards together to produce a single output. SLI is a parallel processing algorithm for computer graphics, meant to increase the available processing power.
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Multisample anti-aliasing (MSAA) is a type of spatial anti-aliasing, a technique used in computer graphics to remove jaggies.
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. Pixels sampled in past frames are blended with pixels sampled in the current frame to produce an anti-aliased image. Although this method makes TAA achieve a result comparable to supersampling, the technique inevitably causes ghosting and blurriness to the image.
Anti-aliasing may refer to any of a number of techniques to combat the problems of aliasing in a sampled signal such as a digital image or digital audio recording.
Fast approximate anti-aliasing (FXAA) is a screen-space anti-aliasing algorithm created by Timothy Lottes at Nvidia.
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Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available in a number of video games. The goal of these technologies is to allow the majority of the graphics pipeline to run at a lower resolution for increased performance, and then infer a higher resolution image from this that approximates the same level of detail as if the image had been rendered at this higher resolution. This allows for higher graphical settings and/or frame rates for a given output resolution, depending on user preference.
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