Look at these pictures.

Both have ~2 million pixels, except one had only 921,600 to begin with and was then upscaled by machine learning model to match the resolution of the other.

Can you guess which one is which?

Image 1


Image 2


A brief history of video games

Before we get to those images, we need to talk a bit about video games.

Over the years video games have started to look so realistic and high quality that they’ve become very computationally intensive.

In simple words you need a lot of processing power to play modern games.

Here’s what a game from 1998 looks like.

Doom 1998

…and here’s one from 2020.

Cyberpunk 2020

The textures, lighting(ray tracing) and graphics in general are just so much better and life like.

It’s becoming hard to tell apart reality from these games, but unfortunately these all these realistic graphics come at a cost of needing to have really high-end hardware.

How games are rendered

Here’s a short video how on 3-D graphics are rendered 👇


3-D objects in the games are rendered by a game ‘engine’ so that you can view them on your flat 2-D screen frame by frame.

These frames are played one after the other in quick succession in order to run the game.

Rendering each of these frames takes some time by the GPU (Graphics Processing Unit).

The faster a GPU is the more frames you can render and smoother the game looks.

The problem

There are 2 things at play here that determine how long it takes to render a frame that you can tweak:

  • The graphics
  • The resolution (how many pixels are rendered)

You basically have a trade off to make here, better looking game, or smoother game.

Or maybe you don’t?

Well the graphics determines how the lighting, textures and all that stuff looks like, that is something we cannot enhance using machine learning.

Different graphic presets

But the resolution is where it gets interesting…

Deep learning for the win!

The more pixels you have in a frame the sharper it is going to look and renders slowly, on the contrary the image looks pixelated and renders quickly when the resolution is lowered.

Different Resolutions

Say hello to DLSS

Nvidia’s DLSS( Deep Learning Super Sampling) is a technology that allows you to render the frames a lower resolution then upscale it with a deep learning model.

This means 2 things, the frames will render quickly (smoother gameplay) and will look sharp.

Nvidia’s DLSS (Deep Learning Super Sampling) does exactly just that.

It is a neural network that is trained on 16K images (a VERY high resolution) and essentially has the ability to fill in pixels and increase the sharpness of an image.


More here

Tensor Cores

In order to perform these machine learning upscaling techniques, RTX series of GPUs have specialised “Tensor” cores, my RTX 3070 has 184 of these.

What’s happening here is:

  1. Game is rendered at a lower resolution
  2. Then upscaled using DLSS.

Which results in…

Smooth gameplay + High resolution

The results

Now finally answering the orignal question, the first image was rendered natively at 1080p, while the second image was rendered at 720p and then upscaled by DLSS.

They’re almost indistinguishable and the game runs buttery smooth. You’ll notice some differences if you zoom in and look at hair and the detail in the rocks.

But considering the model had to generate ~1 million new pixels, this is an extraordinary result, honestly feels like black magic.

These images are from a game called control and here’s the performance, FPS stands for frames per second.

The higher this number, the smoother the game is and you can see the results on these GPUs, this is exciting.


I cannot wait to see the future of AI rendered graphics!

PS: This post was not sponsorsed by Nvidia, I just really love their technology. Would be cool if it was tho :)