We've been waiting to reexamine Nvidia’s Deep Learning Super Sampling for a long time, partly because we wanted new games to come out featuring Nvidia’s updated algorithm. We also wanted to ask Nvidia as many questions as we could to really dig into the current state of DLSS.
Today’s article is going to cover everything. We’ll be looking at the latest titles to use DLSS, focusing primarily on Control and Wolfenstein: Youngblood, to see how Nvidia’s DLSS 2.0 (as we're calling it) stacks up. This will include our usual suite of visual comparisons looking at DLSS compared to native image quality, resolution scaling, and various other post processing techniques. Then, of course, there will be a look at performance across all of Nvidia’s RTX GPUs.
DLSS is at a tipping point. The recently released DLSS 2.0 is clearly an excellent technology and a superb revision that fixes many of its initial issues. It will be a genuine selling point for RTX GPUs moving forward, especially if they can get DLSS in a significant number of games. By the time Nvidia’s next generation of GPUs comes around, DLSS should be ready for prime time and AMD might need to respond in a big way.
Told you guys DLSS just needed a bit of time to train. Now with DLSS 2.0 it no longer needs per game training. Can't wait to see what AMD does with their DirectML competitor.
I'm not saying it doesn't do that, but it also seems to use some TAA according to the source article.
it does seem to be using a temporal reconstruction technique, taking multiple frames and combining them into one for higher detail images. This is evident when viewing some of the fine details around the Control game world, particularly grated air vents, which trouble the image processing algorithm and produce flickering that isn’t present with either the native or 1800p scaled images. These super fine wires or lines throughout the environment seem to consistently give DLSS the most trouble, although image quality for larger objects is decent.