Perceptual Learned Video Compression with Recurrent Conditional GAN
Authors: Ren Yang, Radu Timofte, Luc Van Gool
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that the learned PLVC model compresses video with good perceptual quality at low bit-rate, and that it outperforms the official HEVC test model (HM 16.20) and the existing learned video compression approaches for several perceptual quality metrics and user studies. |
| Researcher Affiliation | Academia | 1ETH Z urich, Switzerland 2Julius Maximilian University of W urzburg, Germany 3KU Leuven, Belgium |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | Yes | The project page is available at https://github.com/RenYang-home/PLVC. |
| Open Datasets | Yes | Our PLVC model is trained on the Vimeo-90k [Xue et al., 2019] dataset. We use the JCT-VC [Bossen, 2013] (Classes B, C and D) and the UVG [Mercat et al., 2020] datasets as the test sets. |
| Dataset Splits | No | The paper states training on Vimeo-90k and testing on JCT-VC and UVG, but does not provide specific details on the training/validation/test splits, percentages, or sample counts for the Vimeo-90k dataset. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU models, CPU types, or cloud computing specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8) for replication. |
| Experiment Setup | Yes | In (4), α, λ and β are hyper-parameters to control the trade-off of bit-rate, distortion and perceptual quality. We set three target bit-rates RT , and set α = α1 when R(yi) RT , and α = α2 α1 when R(yi) < RT to control bit-rates. The hyper-parameters are shown in Table 1. |