# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 4 Conclusions  
In this paper, a spatial prediction-based MC-FRUC algorithm is proposed to improve the MVF accuracy of the interpolated frame. We design a predictive model to partition a frame into the basic  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Ref. [19]**} & \multicolumn{2}{c}{**Ref. [20]**} & \multicolumn{2}{c}{**Ref. [21]**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-7}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline _Football_ & 23.41 & 0.7280 & 24.90 & 0.6820 & 23.26 & 0.6937 & 22.77 & 0.6426 \\ _Foreman_ & 35.02 & 0.9551 & 37.20 & 0.9600 & 34.70 & 0.9494 & 34.93 & 0.9587 \\ _Flower_ & - & - & 30.56 & 0.9689 & 31.76 & 0.9773 & 31.47 & 0.9843 \\ _Mother_ & 46.77 & 0.9800 & 42.84 & 0.9808 & 42.65 & 0.9791 & 42.49 & 0.9809 \\ _Stefan_ & 29.22 & 0.9830 & 29.37 & 0.9395 & 29.26 & 0.9358 & 28.56 & 0.9005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSNR and SSIM comparisons of different MC-FRUC methods for the test sequences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Dual-ME**} & \multicolumn{2}{c}{**DS-ME**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-5}  & **SSIM** & \(\Delta\) & **SSIM** & \(\Delta\) & **SSIM** \\ \hline _Bus_ & 0.8332 & \(-\)0.0765 & 0.7771 & \(-\)0.1326 & 0.9117 \\ _City_ & 0.8645 & \(-\)0.0706 & 0.933 & \(-\)0.0021 & 0.9403 \\ _Football_ & 0.5532 & \(-\)0.0916 & 0.6271 & \(-\)0.0177 & 0.6426 \\ _Foreman_ & 0.9107 & \(-\)0.0399 & 0.9519 & 0.0013 & 0.9587 \\ _Flower_ & 0.9446 & \(-\)0.0329 & 0.9795 & 0.0020 & 0.9843 \\ _Mobile_ & 0.8633 & \(-\)0.0812 & 0.9522 & 0.0077 & 0.9412 \\ _Mother_ & 0.9617 & \(-\)0.0177 & 0.9802 & 0.0008 & 0.9809 \\ _Soccer_ & 0.8661 & \(-\)0.0442 & 0.8988 & \(-\)0.0115 & 0.9145 \\ _Stefan_ & 0.8333 & \(-\)0.0627 & 0.8449 & \(-\)0.0511 & 0.9005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Structural SIMilarity (SSIM) comparison of different ME methods for the test sequences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Dual-ME**} & \multicolumn{2}{c}{**DS-ME**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-5}  & **SSIM** & \(\Delta\) & **SSIM** & \(\Delta\) & **SSIM** \\ \hline _Bus_ & 0.8332 & \(-\)0.0765 & 0.7771 & \(-\)0.1326 & 0.9117 \\ _City_ & 0.8645 & \(-\)0.0706 & 0.933 & \(-\)0.0021 & 0.9403 \\ _Football_ & 0.5532 & \(-\)0.0916 & 0.6271 & \(-\)0.0177 & 0.6426 \\ _Foreman_ & 0.9107 & \(-\)0.0399 & 0.9519 & 0.0013 & 0.9587 \\ _Flower_ & 0.9446 & \(-\)0.0329 & 0.9795 & 0.0020 & 0.9843 \\ _Mobile_ & 0.8633 & \(-\)0.0812 & 0.9522 & 0.0077 & 0.9412 \\ _Mother_ & 0.9617 & \(-\)0.0177 & 0.9802 & 0.0008 & 0.9809 \\ _Soccer_ & 0.8661 & \(-\)0.0442 & 0.8988 & \(-\)0.0115 & 0.9145 \\ _Stefan_ & 0.8333 & \(-\)0.0627 & 0.8449 & \(-\)0.0511 & 0.9005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Structural SIMilarity (SSIM) comparison of different ME methods for the test sequences.  
block and the absent block. The MV of the basic block is first estimated by BME, and then the MV of the absent block is deduced from the MVs of its neighboring basic blocks. To reduce some redundant computations, bilateral SEA is designed to improve BME. Based on the spatial correlation of MV, we have also designed an MV prediction to compute the MV of the absent block. Experimental results show that the proposed ME algorithm can improve both the objective and subjective quality of the interpolated frame with a low computational complexity.  
[PERSON] contributed to the concept of the research, performed the data analysis and wrote the manuscript; [PERSON] helped perform the data analysis with constructive discussions and helped performed the experiment; [PERSON] conducted the experiment and collected the data.  
This work was supported in part by the Innovation Team Support Plan of University Science and Technology of Henan Province (No. 19 IRTSTHN014).  
The authors declare no conflict of interest.