# Internet Video Delivery Improved by Super-Resolution with GAN
## 7 Experimental Results
### Model Parameters and Training  
The training of models used a high-definition image dataset and a server with GPU NVIDIA GeForce GTX 1080 Ti-11 GB, CPU i7-7700, 3.60 GHz, and 62 GB RAM. The testing used a laptop with GPU NVIDIA GeForce GTX 1070 Ti-8 GB, CPU i7-8700, 3.20 GHz, and 32 GB RAM. Models were coded using API Keras ([[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)), accessed on 19 April 2022) and TensorFlow ([[https://www.tensorflow.org](https://www.tensorflow.org)]([https://www.tensorflow.org](https://www.tensorflow.org)), accessed on 19 April 2022). We followed the training methodology associated with each baseline model. Table 3 shows the main parameters defined by each model used in the experimental study. The proposed SR model, i.e., VSRGAN+, was trained using HR and LR images from the Places365-Standard [60] dataset, grouped in batches of 16. We cropped the HR images to produce a set of 128 \(\times\) 128 crop and downscaled each one by 2 using bicubic interpolation and GaussianBlur ([[https://pypi.org/project/opencv-python/](https://pypi.org/project/opencv-python/)]([https://pypi.org/project/opencv-python/](https://pypi.org/project/opencv-python/))). \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Models**} & \multicolumn{1}{c}{**Setup**} \\ \hline \multirow{6}{*}{SRCNN} & Filter = 64, 32, 3 for each layer \\  & Filter size = 9, 1, 5 for each layer, respectively \\  & Optimizer: SGD with a learning rate of \(10^{-4}\) \\  & Batch size: 128 \\  & HR crop size: 33 \(\times\) 33 \\  & Loss function: \(L_{2}\) \\  & Number of iterations = \(8\times 10^{7}\) \\ \hline \multirow{6}{*}{ESPCN} & Filter = 64, 32, \(r^{2}\times 3\) for each layer, respectively \\  & Filter size = 5, 3, 3 for each layer, respectively \\  & Optimizer: Adam with a learning rate of \(10^{-4}\) \\  & Batch size: 128 \\  & HR subimage size: 34 \(\times\) 34 \\  & Loss function: \(L_{2}\) \\  & Number of iterations = \(8\times 10^{7}\) \\ \hline \multirow{6}{*}{CISRDCNN} & Block DBCNN: \(K_{1}-1\) CNN layers use 64 filters \\  & of size \(3\times\) 3 +BN+ReLU, \(K_{1}\)-th layer uses three filters \\  & of size \(3\times\) 3, and uses residual learning \\  & Block USCNN: \(K_{2}-1\) CNN layers use 64 filters \\  & of size \(3\times\) 3 +BN+ReLU, \(K_{2}\)-th is a deconvolutional \\  & layer that uses three filters of size \(9\times 9\) \\  & Block QCNN is similar to DBCNN \\  & Loss function: \(L_{2}\) \\  & \(K_{1}=20\), \(K_{2}=10\), \(K_{3}=10\), and \(QF=20\) \\ \hline \multirow{6}{*}{SRResNet} & Residual blocks: 16 \\  & Optimizer: [PERSON] with a learning rate of \(10^{-4}\) \\ \cline{1-1}  & Batch size: 16 \\ \cline{1-1}  & HR crop size: 96 \(\times\) 96 \\ \cline{1-1}  & Loss function: \(L_{2}\) \\ \cline{1-1}  & Number of iterations = \(10^{6}\) \\ \hline \multirow{6}{*}{SRGAN} & Residual blocks: 16 \\ \cline{1-1}  & Optimizer: [PERSON] with a learning rate of \(10^{-4}\)/learning rate of \(10^{-5}\) \\ \cline{1-1}  & Batch size: 16 \\ \cline{1-1}  & HR crop size: 96 \(\times\) 96 \\ \cline{1-1}  & Loss function: Perceptual loss + adversarial loss \\ \cline{1-1}  & Number of iterations = \(10^{5}/10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model setup. Training based on a large image dataset, such as the Places365-Standard [60], can lead to a general model due to the great diversity of scenes. On the other hand, training with a small database can lead to overfitting, and the trained model can become specialized in restoring a limited set of scenes. As our model does not explore temporal information, we skipped training with videos. Indeed, training using videos can lead to overfitting due to the nature of the video frames, and it takes time for the model to converge and show good performance. The training happens in two steps.