How to get the most out of your GPU subsampling to get the best image quality with your video games

They are already here. At last. PC graphics hardware enthusiasts have been eagerly awaiting the arrival of the first cards in the GeForce RTX 40 family, and NVIDIA hasn’t let us down. A few minutes ago Jensen Huang, the CEO of this American company, presented the new (and brutal) GeForce RTX 4090 and 4080two high-end graphics cards that promise amazing performance when using ray tracing.

However, they have not come alone. Beyond the many novelties that the Ada Lovelace microarchitecture has in store for us, and which we will investigate in a special article that we are already preparing, the GeForce RTX 40 lands backed by image reconstruction technology DLSS 3. And yes, what NVIDIA has promised us during its presentation looks very good. Amazingly well.

This is the promise of DLSS 3: multiply by 4 the performance with ‘ray tracing’

DLSS technology (Deep Learning Super Sampling) from NVIDIA came along with the first generation of GeForce RTX graphics cards from this brand with a promise under its arm: allowing us to enjoy our video games with a higher frame rate per second although our graphic demands were very ambitious. Even when turning on ray tracing.

The purpose of this innovation is to release the GPU of part of the effort involved in rendering the images to increase the rate of frames per second without affecting the graphic quality.

DLSS technology uses real-time analysis of our game frames using deep learning algorithms

The idea is ambitious, and, as users can guess, the technology that makes it possible is complex. In fact, the image reconstruction technique employed by NVIDIA use real-time analytics of the frames of our games using deep learning algorithms.

The strategy used by NVIDIA to alleviate the stress on the GPU is similar to that used by other graphics hardware manufacturers: render resolution is lower to the output resolution that the graphics card finally delivers to our monitor.

Dlss2

In this way, the stress to which the graphics processor is subjected is less, but in exchange it is necessary to resort to a procedure that is responsible for scaling each of the frames from the rendering resolution to the final resolution. Plus, You have to do it efficiently. because, otherwise, the effort that we have avoided in the previous stage could appear in this phase of the generation of the images.

DLSS 3 takes advantage of the 4th generation Tensor cores of GeForce RTX 40 GPUs to run a new reconstruction algorithm called ‘Optical Multi Frame Generation’

This is the phase in which the artificial intelligence developed by NVIDIA comes into action. And the Tensor cores of the GPU. The graphics engine renders images at a lower resolution than we expect, and then DLSS technology scales each frame to the final resolution by applying a deep learning sampling technique to try to recover the maximum level of detail possible.

In the images that we have used to illustrate this article we can see that the procedure implemented in DLSS 3 is more complex than the one used by DLSS 2. In fact, the new NVIDIA image reconstruction technique takes advantage of the presence of the Tensor cores of fourth generation of GeForce RTX 40 GPUs to enable the execution of a new reconstruction algorithm called Optical Multi Frame Generation.

Dlss3

Instead of approaching the reconstruction of each frame working with isolated pixels, which is what DLSS 2 does, this strategy generate full frames. To do this, it analyzes two sequential images of the game in real time and calculates the vector information that describes the movement of all the objects that appear in those frames, but that are not processed by the game engine itself.

According to NVIDIA, this image reconstruction technique manages to multiply by four the rate of images per second provided by DLSS 2

According to NVIDIA, this image reconstruction technique achieves multiply by four the rate of frames per second delivered by DLSS 2. And, also very importantly, it minimizes the aberrations and visual anomalies that appear in some games when using the previous revision of this image reconstruction strategy. It sounds great, so we’re looking forward to testing it to see if its performance is as compelling as NVIDIA is promising.

One more interesting note: the processing of high-resolution frames and motion vectors are fed, as NVIDIA explains, by a convolutional neural network that analyzes all this information and generates a real-time frame additional for each frame processed by the game engine. To conclude, there goes another promise from this company: DLSS 3 can work in tandem with Unity and Unreal Engine, and for the next few months will reach more than 35 games. In fact, it is possible to enable this technique in a short time in those titles that already implement DLSS 2 or Streamline.

Source: www.xataka.com

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Tarun Kumar

Tarun Kumar has worked in the News sector for 05 years and is currently the Owner and Editor of Then24. He reside in Delhi, India with his Family.

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