# AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content

arXiv: https://arxiv.org/abs/2409.17256 · HTML: https://mlsanity.com/paper/2409.17256/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 50 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 30 of 36)

- [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)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://mlsanity.com/paper/2209.11345/) (2022)
- [Rethinking Alignment in Video Super-Resolution Transformers](https://mlsanity.com/paper/2207.08494/) (2022)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [BVI-DVC: A Training Database for Deep Video Compression](https://mlsanity.com/paper/2003.13552/) (2020)
- [AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling](https://mlsanity.com/paper/2111.00772/) (2021)
- [VideoGigaGAN: Towards Detail-rich Video Super-Resolution](https://mlsanity.com/paper/2404.12388/) (2024)
- [AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results](https://mlsanity.com/paper/2208.11184/) (2022)
- [Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report](https://mlsanity.com/paper/2105.08826/) (2021)
- [Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.16484/) (2024)
- [MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution](https://mlsanity.com/paper/2404.09571/) (2024)
- [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)
- [TMP: Temporal Motion Propagation for Online Video Super-Resolution](https://mlsanity.com/paper/2312.09909/) (2023)
- [Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting](https://mlsanity.com/paper/2303.08331/) (2023)
- [Compressing Deep Image Super-resolution Models](https://mlsanity.com/paper/2401.00523/) (2023)
- [AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2408.11982/) (2024)
- [AIM 2024 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2409.14827/) (2024)

## Built on by (most cited first, 7 of 7)

- [The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2504.10686/) (2025)
- [Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion](https://mlsanity.com/paper/2512.23709/) (2025)
- [RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content](https://mlsanity.com/paper/2411.13362/) (2024)
- [FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution](https://mlsanity.com/paper/2502.06431/) (2025)
- [AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results](https://mlsanity.com/paper/2410.04225/) (2024)
- [ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing](https://mlsanity.com/paper/2506.12269/) (2025)
- [Compressed Video Super-Resolution based on Hierarchical Encoding](https://mlsanity.com/paper/2506.14381/) (2025)

## Abstract (arXiv)

Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been developed, they often suffer from high computational demands, resulting in low frame rates (FPS) and poor power efficiency, especially on mobile platforms. In this work, we compile different methods to address these challenges, the solutions are end-to-end real-time video super-resolution frameworks optimized for both high performance and low runtime. We also introduce a new test set of high-quality 4K videos to further validate the approaches. The proposed solutions tackle video up-scaling for two applications: 540p to 4K (x4) as a general case, and 360p to 1080p (x3) more tailored towards mobile devices. In both tracks, the solutions have a reduced number of parameters and operations (MACs), allow high FPS, and improve VMAF and PSNR over interpolation baselines. This report gauges some of the most efficient video super-resolution methods to date.