# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 4 Experimental Results
### Comparisons with State-of-the-Arts
#### 4.3.2 Comparison of Experimental Results on the SPMCS-11 Test Set  
We tested our method on the SPMCS-11 test set, which consists of \(11\) high-quality video clips with different actions and scenes. Compared to Vid4, SPMCS-11 contains more high-frequency information at higher resolution, which requires the algorithm to have strong recovery capabilities, in comparison with existing methods including Bicubic, RBPN [9], TGA [29], and RRN [21]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Params (M)** & **Runtimes (ms)** & **Calendar** & **City** & **Foliage** & **Walk** & **Average** \\ \hline Bicubic & - & - & 18.80/0.4794 & 23.78/0.5129 & 21.39/0.4227 & 22.88/0.6976 & 21.71/0.5280 \\ FRVSR & 5.1 & 137 & 23.46/0.7854 & 27.70/0.8099 & 25.96/0.7560 & 29.69/0.8990 & 26.70/0.8126 \\ DUF & 5.8 & 974 & 24.04/0.8110 & 28.27/0.8313 & 26.41/0.7709 & 30.30/0.9141 & 27.33/0.8318 \\ PFNL & 3.0 & 295 & - & - & - & 27.16/0.8355 \\ RBPN & 12.2 & 1507 & 23.95/0.8070 & 27.70/0.8036 & 26.22/0.7575 & 30.69/0.9104 & 27.14/0.8196 \\ RLSP & 4.2 & 49 & 24.60/0.8355 & 28.14/0.8453 & 26.75/0.7983 & 30.88/0.9192 & 27.60/0.8476 \\ TGA & 5.8 & 441 & 24.50/0.8290 & 28.50/0.8420 & 26.59/0.7793 & 30.95/0.9171 & 27.63/0.8419 \\ RRN & 3.4 & 45 & 24.57/0.8342 & 28.51/0.8467 & 26.94/0.7979 & 30.74/0.9164 & 27.69/0.8488 \\ BasicVSR & 6.3 & 63 & - & - & - & - & 27.96/0.8553 \\ GHLRF & 20.4 & 110 & 25.02/0.8482 & 30.09/0.8822 & 26.95/0.7961 & 31.34/0.9253 & 28.60/0.8630 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test results on the Vid4 dataset using different video super-resolution methods. As shown in Table 5, among all VSR methods supporting the SPMCS-11 test set approach, our model achieved the best results in terms of both PSNR and SSIM. Compared to RRN and TGA, our model had better performance of 1.06 dB and 0.56 dB, respectively. As can be seen from Table 5. our method obtained the highest PSNR and SSIM values in the seven tested videos compared to the other SISR and VSR methods. A qualitative comparison of the SPMCS-11 test set is shown in Figures 12 and 13. It can be observed that the method in this paper is better at recovering more detail and is closer to the real image. On the \"PRVTG\", the white border of the window is better displayed by  
Figure 11: Comparison of the reconstruction results of \(\times\)4 on Calendar for different video super-resolution methods. Figure 10: Comparison of the reconstruction results of \(\times\)4 on City for different video super-resolution methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clip Name** & **Bicubic** & **RBPN** & **RRN** & **TGA** & **GHLRF** \\ \hline car05\_001 & 24.94/0.6799 & 31.92/0.9016 & 31.79/0.9048 & 31.84/0.8987 & 32.70/0.9192 \\ hdclub\_003\_001 & 17.54/0.9186 & 21.89/0.7246 & 22.42/0.7633 & 22.31/0.7537 & 22.74/0.7764 \\ hitachi\_ise5\_001 & 17.44/0.4283 & 26.25/0.9042 & 26.45/0.9058 & 26.47/0.9059 & 27.88/0.9327 \\ hk004\_001 & 25.93/0.7221 & 33.34/0.9010 & 33.48/0.9138 & 33.76/0.9136 & 34.23/0.9213 \\ HKVTG\_004 & 25.29/0.8765 & 29.50/0.7975 & 29.70/0.8900 & 29.68/0.8088 & 29.94/0.8187 \\ jvc\_009\_001 & 23.15/0.6713 & 29.99/0.9093 & 29.52/0.9038 & 30.19/0.9140 & 31.27/0.9326 \\ NYVTG\_006 & 25.69/0.7291 & 33.18/0.9227 & 33.30/0.9222 & 33.75/0.9334 & 34.00/0.9377 \\ PYVTG\_012 & 23.74/0.6249 & 27.56/0.8231 & 27.98/0.8403 & 27.80/0.8350 & 27.94/0.8434 \\ RMVTG\_011 & 20.98/0.5137 & 27.59/0.8157 & 28.20/0.8351 & 28.35/0.8409 & 28.45/0.8461 \\ veni3\_011 & 25.73/0.8312 & 36.58/0.9735 & 34.17/0.9695 & 36.40/0.9740 & 37.15/0.9774 \\ Veni5\_015 & 24.59/0.7823 & 32.92/0.9443 & 31.50/0.9411 & 33.24/0.9486 & 33.54/0.9517 \\ \hline Average & 23.18/0.6253 & 30.06/0.8743 & 29.84/0.8827 & 30.34/0.8842 & 30.90/0.9861 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results on the SPMCS-11 dataset using different video super-resolution methods. our method; on the \"Hitachi\", our method shows a better visual effect, and on the labelled frame, our method is closer to that of the \"GT\" diagram.