# Deep Learning Techniques for Super-Resolution in Video Games

arXiv: https://arxiv.org/abs/2012.09810 · HTML: https://mlsanity.com/paper/2012.09810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling](https://mlsanity.com/paper/2508.17756/) (2025)
- [An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling](https://mlsanity.com/paper/2107.07647/) (2021)
- [SyntheticFur dataset for neural rendering](https://mlsanity.com/paper/2105.06409/) (2021)
- [Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning](https://mlsanity.com/paper/2412.08950/) (2024)
- [Super-Resolution Generative Adversarial Networks based Video Enhancement](https://mlsanity.com/paper/2505.10589/) (2025)

## Abstract (arXiv)

The computational cost of video game graphics is increasing and hardware for processing graphics is struggling to keep up. This means that computer scientists need to develop creative new ways to improve the performance of graphical processing hardware. Deep learning techniques for video super-resolution can enable video games to have high quality graphics whilst offsetting much of the computational cost. These emerging technologies allow consumers to have improved performance and enjoyment from video games and have the potential to become standard within the game development industry.