# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 4 Experimental Results and Analyses
### Objective Evaluation  
In our evaluation, two widely used metrics for video quality, peak signal to noise ratio (PSNR) and structural similarity index (SSIM) [43], are adopted for objective evaluation. The objective performance of the proposed method is shown in Table 5. Higher PSNR but lower SSIM is achieved with refinement. The proposed method is first compared with the MCode framework, a frame interpolation approach proposed by [PERSON] et al. [7]. Tested on the KTH (hand-waving) dataset [44], the MCode gives an average PSNR of 22.85 dB and SSIM of 0.609 for the interpolated frames. The proposed method, on the other hand, provides a much higher average PSNR of 33.18 dB and SSIM of 0.919. The \(p\) value of the associated statistical hypothesis test equal to 0.01 substantiates the significantly better performance of the proposed framework.  
The proposed method is further compared with [1] (SepConv, the state-of-the-art-solution), and the performance is shown in Table 6. All the approaches have comparable quality metrics, while the proposed approaches have more stable performance (less standard deviation in PSNR and SSIM), and the proposed method (without refinement) has a slightly higher SSIM. This framework also requires less time (80 h) for training, wherein contrast SepConv requires 216 h. The computation complexity of the proposed method (network) is 0.78 GFLOPs (without refinement) and 1.08 GFLOPs (with refinement) for processing 64-by-64-pixel block(s). Additionally, without applying the refinement step, our pre-trained framework takes approximately 60 ms to process a frame that has a resolution of 320-by-240 from the UCF101 dataset, which is much less than that of SepConv. As a consequence, more than 16 frames can be generated in a second, and the frame rate up-conversion for 320-by-240 videos from 15 fps to 30 fps can be achieved. Figure 4 illustrates a good performance achieved by the proposed method in terms of the quality (PSNR/SSIM) and complexity (time) relationship.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Number of epochs & 400 \\ Batch size & 32 \\ Size of input image & 64 \\ Input’s channels & 3 \\ Learning rate & 0.0002 \\ [PERSON]: \(\beta\)1 & 0.5 \\ [PERSON]: \(\beta\)2 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Configuration parameters.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Proposed Method**} & \multicolumn{2}{c}{**Proposed Method**} \\  & \multicolumn{2}{c}{**(without Refinement)**} & \multicolumn{2}{c}{**(with Refinement)**} \\ \hline
**Dataset** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline UCF101:Body Motion Only & 28.97 & 0.827 & 29.52 & 0.809 \\ UCF101:Human-Human Interaction & 27.50 & 0.789 & 27.60 & 0.762 \\ UCF101:Human-Object Interaction & 31.37 & 0.867 & 31.72 & 0.832 \\ UCF101:Playing Musical Instrument & 32.54 & 0.881 & 32.48 & 0.852 \\ UCF101:Sport & 29.10 & 0.821 & 29.42 & 0.797 \\ CUHK & 32.63 & 0.935 & 32.20 & 0.910 \\ Sintel & 32.64 & 0.914 & 33.11 & 0.912 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Objective evaluation on various datasets for the proposed method.