AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913518401355776 |
|---|---|
| author | Conde, Marcos V Lei, Zhijun Li, Wen Bampis, Christos Katsavounidis, Ioannis Timofte, Radu |
| author_facet | Conde, Marcos V Lei, Zhijun Li, Wen Bampis, Christos Katsavounidis, Ioannis Timofte, Radu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content Conde, Marcos V Lei, Zhijun Li, Wen Bampis, Christos Katsavounidis, Ioannis Timofte, Radu Image and Video Processing Computer Vision and Pattern Recognition Graphics Multimedia 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. |
| title | AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Graphics Multimedia |
| url | https://arxiv.org/abs/2409.17256 |