Deep Learning Techniques for Super-Resolution in Video Games
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 2.
5 papers.
- 2025SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tilingcited by 6
- 2021An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsamplingcited by 4
- 2021SyntheticFur dataset for neural renderingcited by 0
- 2024Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learningcited by 0
- 2025Super-Resolution Generative Adversarial Networks based Video Enhancementcited by 0
- 2025SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling25 Aug 2025
- 2025Super-Resolution Generative Adversarial Networks based Video Enhancement14 May 2025
- 2024Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning12 Dec 2024
- 2021An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling15 Jul 2021
- 2021SyntheticFur dataset for neural rendering13 May 2021