# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 4 Experimental Results and Analyses
### Subjective Results  
Examples of the generated frames are shown in Figures 5-7. It can be observed that the proposed method maintains correct motion and most textural details in the scene. Besides, the inclusion of the refinement network derives the motion areas more favorably. For a better point of view on the similarity between the ground truth and the generated frames, the histograms of pixels are graphed in Figure 8, which illustrate their similarity. The correlation coefficients between the histograms confirm the accuracy of the proposed method and the advantage of incorporating the refinement process. In Figure 9, we compare the generated outputs of the proposed method with SepConv [1]. It can be seen that the proposed approach keeps more structural details of the objects in the scene. Besides, the refinement process improves the estimation of color and motion (Figure 7). We do an ablation study on the loss functions. Using a combination of the adversarial loss, L1 loss, and GDL yields a PSNR of 27.97 dB and SSIM of 0.816. Using a combination of the adversarial loss, L2 loss, and GDL yields a PSNR of 27.84 dB and SSIM of 0.746. The proposed combination of four loss functions, including the MS-SSIM loss, yields PSNR of 28.62 dB and SSIM of 0.810. Figure 10 shows the generated frames. The proposed method creates frames that keep more details. In summarization, our method interpolates a frame from two given inputs within a short time. Moreover, the output is correct in motion, details, and color. Without the refinement, our framework produces preferable frames in human vision. The refinement network improves the output frame in the similarity to the ground truth, although it may reduce the frame's quality in some cases. Hence, each approach is suitable for different kinds of applications. The proposed method may not be able to capture irregular motion correctly. More training sequences or a conditional GAN model will be tested in the future. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Training** & **Processing** & \multicolumn{4}{c}{**PSNR**} & \multicolumn{4}{c}{**SSIM**} \\ \cline{3-10}  & **Time** & **Time** & **Min** & **Max** & **Avg** & **Sidev** & **Min** & **Max** & **Avg** & **Sidev** \\ \hline SepConv [1] & 216 & 340 & 12.53 & 43.44 & **30.16** & 5.90 & 0.161 & **0.998** & 0.829 & 0.136 \\ Ours (w/o refinement) & **80** & **60** & 12.60 & 39.83 & 29.22 & **4.97** & **0.170** & 0.984 & **0.835** & 0.126 \\ Ours (with refinement) & 167 & 160 & **12.61** & **44.67** & 29.84 & 5.58 & 0.169 & 0.972 & 0.816 & **0.120** \\ \hline \hline \end{tabular}  
* Categories action from the UCF101\(\_\)Body Motion Only dataset. 2 The videos have a resolution of 320-by-240-pixel.  
\end{table} 
Table 6: Comparisons of the proposed method and SepConv [44] on UCF101 1,2 The best value of each column is in bold. Figure 4: The average metric values, **(a)** PSNR and **(b)** SSIM, with respecting the processing time of various approaches, which include SepConv (black), the proposed approach without the refinement (blue), and the proposed approach with the refinement (green), to derive a 64-by-64-pixel output. Figure 5: Experimental results on the CUHK dataset. The ground truth is shown in the first row and the generated outputs are shown in the second row: (**a**) The full ground truth, (**b**) the ground truth, (**c**) the output of the proposed method without refinement (PSNR = 32.87, SSIM = 0.935), (**d**) the output of the proposed method with refinement (PSNR = 31.39, SSIM = 0.914). Figure 6: Experimental results on the Sintel datasets. The figures are demonstrated in two rows: (**a**) The full ground truth, (**b**) the ground truth, (**c**) the output of the proposed method without refinement (PSNR = 27.96, SSIM = 0.860), (**d**) the output of the proposed method with refinement (PSNR = 29.08, SSIM = 0.871).