SAVSR: Arbitrary-Scale Video Super-Resolution via a Learned Scale-Adaptive Network
Authors: Zekun Li, Hongying Liu, Fanhua Shang, Yuanyuan Liu, Liang Wan, Wei Feng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments at various scales on the benchmark datasets show that the proposed SAVSR outperforms state-of-the-art (SOTA) methods at non-integer and asymmetric scales. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Xidian University, China 2Medical College, Tianjin University, Tianjin, China 3College of Intelligence and Computing, Tianjin University, Tianjin, China 4Peng Cheng Lab, Shenzhen, China |
| Pseudocode | No | The paper describes the network architecture and modules in text and diagrams (e.g., Figure 2), but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/Weepingchestnut/SAVSR. |
| Open Datasets | Yes | We use the training set from Vimeo-90K (Xue et al. 2019) dataset which contains over 9000 training and testing video sequences. |
| Dataset Splits | No | The paper states it uses the training set of Vimeo-90K for training and Vid4 and UDM10 for evaluation, but it does not explicitly provide details about a validation split used for hyperparameter tuning or early stopping during training, especially not from the Vimeo-90K dataset itself. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Following the arbitrary-scale SISR works (Hu et al. 2019; Wang et al. 2021; Chen, Liu, and Wang 2021), for some non-integer scales we crop the frame borders to ensure that downsampling these scales does not result in fractional resolution, e.g., a 576 × 704 frame must be cropped to 575 × 700 when downsampling by 2.5, to make the LR frame with an integer resolution 230 × 280. Considering the trade-off between performance and complexity, we set the iteration window size to 7 and the sliding window size to 3 for the Vimeo90K dataset. |