Deep learning anti-aliasing
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 such as raw low-resolution input, motion vectors, depth buffers, and exposure information. This information is then used by DLAA to improve its anti-aliasing, with the aim of reducing temporal instability.
Differences between TAA and DLAA
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.
Differences between DLSS and DLAA
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.[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.
See also
References
- ^ a b Kostovic, Aleksandar (2021-09-20). "Nvidia Readies Deep Learning Anti-Aliasing Debut with The Elder Scrolls Online Update". Tom's Hardware. Retrieved 2022-02-20.
- ^ Hruska, Joel (2021-09-21). "Nvidia's DLAA Could Be a Huge Step Forward for Anti-Aliasing". ExtremeTech.
- ^ a b Liu, Edward (2020-03-23). "DLSS 2.0 – Image Reconstruction for Real-Time Rendering With Deep Learning" (PDF). Behind the Pixels.
- ^ "Nvidia's DLAA makes PC games look better with little performance hit". PCWorld. Retrieved 2024-04-20.
- ^ Yang, Lei; Liu, Shiqiu; Salvi, Marco. "A Survey of Temporal Antialiasing Techniques" (PDF). Computer Graphics Forum. 39 (2): 607–621. doi:10.1111/cgf.14018 – via Behind the Pixels.
- ^ De Meo, Francesco (2021-09-23). "The Elder Scrolls Online DLAA vs DLSS vs TAA Comparison Video Highlights DLAA Superior Image Quality". Wccftech. Retrieved 2022-02-20.
- ^ Karis, Brian. "High Quality Temporal Supersamplin" (PDF).
- ^ "GTC 2020: DLSS 2.0 - Image Reconstruction for Real-time Rendering with Deep Learning". NVIDIA Developer. 2020-06-09. Retrieved 2022-06-26.
- ^ "NVIDIA DLSS 2.0: A Big Leap In AI Rendering". www.nvidia.com. Retrieved 2022-06-26.
- ^ maxus24; on; Studios, in Game Testing Manufacturer: Zenimax Online (2021-09-22). "NVIDIA DLAA Anti-Aliasing Review - DLSS at Native Resolution". TechPowerUp. Retrieved 2024-03-22.
{{cite web}}: CS1 maint: numeric names: authors list (link) - ^ Archer, James (2022-06-27). "Nvidia DLAA: How it works, supported games and performance vs DLSS". Rock, Paper, Shotgun. Retrieved 2022-07-09.