Template-Type: ReDIF-Article 1.0
Title: Impact of NVIDIA DLSS on Graphics Performance in Modern Video Games
Author-Name: Romero, Yennifer Díaz
Author-Name: Segura, Ángel Santiago Salazar
Author-Name: García, Víctor Manuel Martínez
Author-Name: Lizárraga, Pedro Antonio Valdez
Abstract: This study analyzed the impact of NVIDIA's Deep Learning Super Sampling (DLSS) technology on the graphics performance of modern video games through a qualitative, descriptive documentary research approach. Six scientific articles on super-resolution, convolutional neural networks, temporal reconstruction, and data-driven rendering were reviewed to identify the theoretical foundations underlying DLSS. The findings indicate that this technology integrates advances in deep learning to reconstruct high-quality images from lower-resolution inputs, improving graphics performance while maintaining visual quality. Furthermore, DLSS shares key principles with models such as SRGAN and ESRGAN, particularly the use of spatial and temporal information to optimize image reconstruction. It is concluded that DLSS represents a practical application of scientific advances in super-resolution, providing an efficient solution for real-time graphics processing.
Journal: SAP Social AI
Year: 2026
DOI: 10.62486/sai202684
File-URL: https://southam.pub/journals/files/sai/sai202684en.pdf
File-Format: application/pdf
Handle: RePEc:cwf:saiart:sai202684