# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 3 Experimental Results
### Objective Evaluation  
Table 1 shows the PSNR values for different FRUC techniques. For the nine test video sequences, the proposed algorithm significantly improves the PSNR value by a maximum of 2.72 dB and achieves the purpose of improving the quality of the interpolated frame when compared with the Dual-ME method. The DS-ME method can be effectively utilized in the video sequences which are relatively static or contain fewer motions, such as _Foreman_ and _Mother_. But for videos containing a large number of movements, such as _Bus_, _City_, _Football_, _Mobile_ and _Stefan_, our algorithm can track the object's trajectory more precisely, and the PSNR value can be improved by up to 3.21 dB when compared with DS-ME. The SSIM values of different FRUC techniques are shown in Table 2. By comparison, we can see that our proposed algorithm is significantly relatively better than the comparing methods and is only less effective than DS-ME in processing static videos. As is shown in Table 3, the running time of our algorithm is shorter than the DS-ME method and the Dual-ME method, which indicates that the spatial algorithm provides a lower computational complexity. It can be seen that the computational resource configuration of the proposed algorithm is more effective. By partitioning the frame and combining the advantages of a full search ME and spatial correlation, the calculation time can be saved while ensuring accuracy at the same time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Dual-ME**} & \multicolumn{2}{c}{**DS-ME**} & \multicolumn{1}{c}{**Proposed**} \\ \cline{2-6}  & **PSNR(dB)** & \(\Delta\) **(dB)** & **PSNR(dB)** & \(\Delta\) **(dB)** & **PSNR(dB)** \\ \hline _Bus_ & 25.02 & \(-1.59\) & 23.4 & \(-3.21\) & 26.61 \\ _City_ & 31.94 & \(-1.64\) & 33.43 & \(-0.15\) & 33.58 \\ _Football_ & 22.78 & 0.01 & 22.43 & \(-0.34\) & 22.77 \\ _Foreman_ & 32.37 & \(-2.56\) & 35.17 & 0.24 & 34.93 \\ _Flower_ & 28.75 & \(-2.72\) & 32.14 & 0.67 & 31.47 \\ _Mobile_ & 25.33 & \(-2.36\) & 27.54 & \(-0.15\) & 27.69 \\ _Mother_ & 40.81 & \(-1.68\) & 43.04 & 0.55 & 42.49 \\ _Soccer_ & 25.96 & \(-2.39\) & 28.68 & 0.33 & 28.35 \\ _Stefan_ & 27.03 & \(-1.53\) & 27.55 & \(-1.01\) & 28.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak Signal-to-Noise Ratio (PSNR) (dB) comparison of different Motion Estimation (ME) methods for the test sequences.  
Figure 5: A visual comparison of the _Stefan_ sequence between the three methods.  
Table 4 presents PSNR and SSIM results of the proposed algorithm and three MC-FRUC algorithms proposed by References [19; 20; 21], respectively. The results of References [19; 20; 21] are directly taken from their originals. From Table 4, we can see that our algorithm obtains PSNR gains for several test sequences, e.g., the proposed algorithm is 0.23 dB higher than in Reference [21] for _Foreman_ and 0.91 dB higher than in Reference [20] for _Flower_. In most cases, the PSNR values of our algorithm have the comparative results with References [19; 20; 21]. For SSIM evaluations, our algorithm obtains the highest SSIM values among all algorithms for _Foreman_, _Flower_ and _Mother_ but has the obvious SSIM degradations for sport sequences _Football_ and _Stefan_, indicating that our algorithm can recover the good structures for sequences with low and medium motions. From above, it can be seen that our algorithm is competitive in terms of interpolation quality when compared with References [19; 20; 21]. Otherwise, in terms of computational complexity, our algorithm has advantages over References [19; 20; 21], e.g., [PERSON] et al. [20] costs 39.41 s on average to interpolate a CIF frame, but our algorithm only costs 4.35 s on average to interpolate a CIF frame.