This paper addresses the challenge of keeping up with the ever-increasing graphical complexity of video games and introduces a deep-learning approach to mitigating it. As games get more and more demanding in terms of their graphics, it becomes increasingly difficult to maintain high-quality images while also ensuring good performance. This is where deep learning super sampling (DLSS) comes in. The paper explains how DLSS works, including the use of convolutional autoencoder neural networks and various other techniques and technologies. It also covers how the network is trained and optimized, as well as how it incorporates temporal antialiasing and frame generation techniques to enhance the final image quality. We will also discuss the effectiveness of these techniques as well as compare their performance to running at native resolutions.
"Deep-Learning Realtime Upsampling Techniques in Video Games,"
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 10:
2, Article 4.
Available at: https://digitalcommons.morris.umn.edu/horizons/vol10/iss2/4