# RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content

arXiv: https://arxiv.org/abs/2411.13362 · HTML: https://mlsanity.com/paper/2411.13362/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation](https://mlsanity.com/paper/2306.09818/) (2023)
- [BVI-DVC: A Training Database for Deep Video Compression](https://mlsanity.com/paper/2003.13552/) (2020)
- [CANF-VC: Conditional Augmented Normalizing Flows for Video Compression](https://mlsanity.com/paper/2207.05315/) (2022)
- [NVRC: Neural Video Representation Compression](https://mlsanity.com/paper/2409.07414/) (2024)
- [MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution](https://mlsanity.com/paper/2404.09571/) (2024)
- [MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering](https://mlsanity.com/paper/2007.07099/) (2020)
- [BVI-AOM: A New Training Dataset for Deep Video Compression Optimization](https://mlsanity.com/paper/2408.03265/) (2024)
- [CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement](https://mlsanity.com/paper/2011.09190/) (2020)
- [ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation](https://mlsanity.com/paper/2302.08455/) (2023)
- [Compressing Deep Image Super-resolution Models](https://mlsanity.com/paper/2401.00523/) (2023)
- [AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content](https://mlsanity.com/paper/2409.17256/) (2024)
- [ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing](https://mlsanity.com/paper/2111.15536/) (2021)
- [Towards Hybrid-Optimization Video Coding](https://mlsanity.com/paper/2207.05565/) (2022)
- [A Video Coding Method Based on Neural Network for CLIC2024](https://mlsanity.com/paper/2401.03623/) (2024)

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

- [The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2504.10686/) (2025)
- [HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution](https://mlsanity.com/paper/2412.03748/) (2024)
- [Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion](https://mlsanity.com/paper/2512.23709/) (2025)
- [Compressed Video Super-Resolution based on Hierarchical Encoding](https://mlsanity.com/paper/2506.14381/) (2025)
- [Trajectory-aware Shifted State Space Models for Online Video Super-Resolution](https://mlsanity.com/paper/2508.10453/) (2025)

## Abstract (arXiv)

Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been employed in many applications including video streaming, where compressed low-resolution content is typically transmitted to end users and then reconstructed with a higher resolution and enhanced quality. To support real-time playback, it is important to implement fast SR models while preserving reconstruction quality; however most existing solutions, in particular those based on complex deep neural networks, fail to do so. To address this issue, this paper proposes a low-complexity SR method, RTSR, designed to enhance the visual quality of compressed video content, focusing on resolution up-scaling from a) 360p to 1080p and from b) 540p to 4K. The proposed approach utilizes a CNN-based network architecture, which was optimized for AV1 (SVT)-encoded content at various quantization levels based on a dual-teacher knowledge distillation method. This method was submitted to the AIM 2024 Video Super-Resolution Challenge, specifically targeting the Efficient/Mobile Real-Time Video Super-Resolution competition. It achieved the best trade-off between complexity and coding performance (measured in PSNR, SSIM and VMAF) among all six submissions. The code will be available soon.