| # Frequently Asked Questions | |
| ### 1. How to reproduce your results in the [PIRM18-SR Challenge](https://www.pirm2018.org/PIRM-SR.html) (with low perceptual index)? | |
| First, the released ESRGAN model in the GitHub (`RRDB_ESRGAN_x4.pth`) is **different** from the model we submitted in the competition. | |
| We found that the lower perceptual index does not always guarantee a better visual quality. | |
| The aims for the competition and our ESRGAN work will be a bit different. | |
| We think the aim for the competition is the lower perceptual index and the aim for our ESRGAN work is the better visual quality. | |
| > More analyses can be found in Sec 4.1 and Sec 5 in [PIRM18-SR Chanllenge report](https://arxiv.org/pdf/1809.07517.pdf). | |
| > It points out that PI (perceptual index) is well correlated with the human-opinion-scores on a coarse scale, but it is not always well-correlated with these scores on a finer scale. This highlights the urgent need for better perceptual quality metrics.) | |
| Therefore, in the PIRM18-SR Challenge competition, we used several tricks for the best perceptual index (see Section 4.5 in the [paper](https://arxiv.org/abs/1809.00219)). | |
| Here, we provid the models and codes used in the competition, which is able to produce the results on the `PIRM test dataset` (we use MATLAB 2016b/2017a): | |
| | Group | Perceptual index | RMSE | | |
| | ------------- |:-------------:| -----:| | |
| | SuperSR | 1.978 | 15.30 | | |
| > 1. Download the model and codes from [GoogleDrive](https://drive.google.com/file/d/1l0gBRMqhVLpL_-7R7aN-q-3hnv5ADFSM/view?usp=sharing) | |
| > 2. Put LR input images in the `LR` folder | |
| > 3. Run `python test.py` | |
| > 4. Run `main_reverse_filter.m` in MATLAB as a post processing | |
| > 5. The results on my computer are: Perceptual index: **1.9777** and RMSE: **15.304** | |
| ### 2. How do you get the perceptual index in your ESRGAN paper? | |
| In our paper, we provide the perceptual index in two places. | |
| 1). In the Fig. 2, the perceptual index on PIRM self validation dataset is obtained with the **model we submitted in the competition**. | |
| Since the pupose of this figure is to show the perception-distortion plane. And we also use the post-precessing here same as in the competition. | |
| 2). In the Fig.7, the perceptual indexs are provided as references and they are tested on the data generated by the released ESRGAN model `RRDB_ESRGAN_x4.pth` in the GiuHub. | |
| Also, there is **no** post-processing when testing the ESRGAN model for better visual quality. | |