I’ve been testing and playing around with DLSS for the last few weeks. Personally, I’ve found it to be fantastic and deliver great performance boosts at decent image quality. However, I have seen plenty of reports to the contrary, with some reporting overly blurry images. So, what’s going on? Nvidia’s Technical Director of Deep Learning had plenty to say.
It’s quite a wall of text but keep with it, there’s some good information here. Basically, DLSS doesn’t need to be used if your framerate is already decent. DLSS will improve with updates, and the upscaling engine is always learning. I can’t wait to see it improve, and I’m sure some gamers even more so!
“DLSS is designed to boost frame rates at high GPU workloads (i.e. when your framerate is low and your GPU is working to its full capacity without bottlenecks or other limitations). If your game is already running at high frame rates, your GPU’s frame rendering time may be shorter than the DLSS execution time. In this case, DLSS is not available because it would not improve your framerate. However, if your game is heavily utilizing the GPU (e.g. FPS is below ~60), DLSS provides an optimal performance boost. You can crank up your settings to maximize your gains. (Note: 60 FPS is an approximation — the exact number varies by game and what graphics settings are enabled)
To put it a bit more technically, DLSS requires a fixed amount of GPU time per frame to run the deep neural network. Thus, games that run at lower frame rates (proportionally less fixed workload) or higher resolutions (greater pixel shading savings), benefit more from DLSS. For games running at high frame rates or low resolutions, DLSS may not boost performance. When your GPU’s frame rendering time is shorter than what it takes to execute the DLSS model, we don’t enable DLSS. We only enable DLSS for cases where you will receive a performance gain. DLSS availability is game-specific, and depends on your GPU and selected display resolution.”
“We built DLSS to leverage the Turing architecture’s Tensor Cores and to provide the largest benefit when GPU load is high. To this end, we concentrated on high resolutions during development (where GPU load is highest) with 4K (3840×2160) being the most common training target. Running at 4K is beneficial when it comes to image quality as the number of input pixels is high. Typically for 4K DLSS, we have around 3.5-5.5 million pixels from which to generate the final frame, while at 1920×1080 we only have around 1.0-1.5 million pixels. The less source data, the greater the challenge for DLSS to detect features in the input frame and predict the final frame.
We have seen the screenshots and are listening to the community’s feedback about DLSS at lower resolutions, and are focusing on it as a top priority. We are adding more training data and some new techniques to improve quality, and will continue to train the deep neural network so that it improves over time.”
“For Battlefield V, we think DLSS delivers a big improvement in 4K and 2560×1440 performance — up to 40% — for the corresponding quality, but also hear the community. For the next push, we are focusing our testing and training to improve the image quality at 1920×1080 and also for ultrawide monitors (e.g. 3440×1440). The current experience at these resolutions is not where we want them.
“For Metro Exodus, we’ve got an update coming that improves DLSS sharpness and overall image quality across all resolutions that didn’t make it into day of launch. We’re also training DLSS on a larger cross section of the game, and once these updates are ready you will see another increase in quality. Lastly, we are looking into a few other reported issues, such as with HDR, and will update as soon as we have fixes.”
Have you tried DLSS on the RTX cards? What do you think, is it working well for you or is it a bit blurry?
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