JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks
Authors: Menglu Wang, Xueyang Fu, Zepei Sun, Zheng-Jun Zha
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, China 2Xidian University, China |
| Pseudocode | No | The paper describes methods and architectures but does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide a link to source code or explicitly state that the code is publicly available. |
| Open Datasets | Yes | In our experiments, we use four datasets: DIV2K [Agustsson and Timofte, 2017], BSDS500 [Arbelaez et al., 2010], LIVE1 [Sheikh, 2005] and Classic5 [Zeyde et al., 2010]. |
| Dataset Splits | No | The paper states 'The DIV2K dataset (800 images) is used for training, while the other three datasets are used for testing,' but does not specify a separate validation dataset or its split details. |
| Hardware Specification | Yes | We implement the proposed model with two Titan Xp GPUs by using Py Torch. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | Yes | The parameters α and β are empirically set as 0.01 and 0.9, respectively. The learning rate is initialized to 10 4 and decreased to half every 1 105 iterations. The ε in margin ranking loss is set to 0.5. For optimizations, we use Adam optimizer [Kingma and Ba, 2014] to train our model. |