Transcoded Video Restoration by Temporal Spatial Auxiliary Network
Authors: Li Xu, Gang He, Jinjia Zhou, Jie Lei, Weiying Xie, Yunsong Li, Yu-Wing Tai2875-2883
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that the performance of the proposed method is superior to that of the previous techniques. We quantitatively and qualitatively demonstrate our proposed method is superior to that of the previous methods. |
| Researcher Affiliation | Collaboration | Li Xu1, Gang He1,2, , Jinjia Zhou3, Jie Lei1, Weiying Xie1, Yunsong Li1, Yu-Wing Tai2 1Xidian University, China 2Kuaishou Technology, China 3Hosei University, Japan |
| Pseudocode | No | The paper provides architectural diagrams and textual descriptions of its modules but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/icecherylXuli/TSAN. |
| Open Datasets | Yes | To establish a training dataset for video transcoding restoration, we employed 108 sequences from Xiph.org (Xiph.org), VQEG (VQEG), and Joint Collaborative Team on Video Coding (JCT-VC) (Bossen et al. 2013). |
| Dataset Splits | Yes | To establish a training dataset for video transcoding restoration, we employed 108 sequences from Xiph.org (Xiph.org), VQEG (VQEG), and Joint Collaborative Team on Video Coding (JCT-VC) (Bossen et al. 2013). We adopt all 18 standard test sequences from JCT-VC for testing. |
| Hardware Specification | Yes | We implement our TSAN with Pytorch 1.6.0 framework on a NVIDIA Ge Force 2080Ti GPU |
| Software Dependencies | Yes | We implement our TSAN with Pytorch 1.6.0 framework |
| Experiment Setup | Yes | The batch size is set to 16 and the learning rate is initialized as 1e-4. The network training stops after 300k iterations. |