# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 5 Conclusions  
We proposed a neural-network-based quality enhancement for the reference CU to improve the accuracy of inter prediction. The network takes the luma reference CU before motion compensation as input. The output quality-enhanced blocks of the network are then interpolated by a DCTIF to generate a fractional motion compensation block. The proposed method was also implemented for affine motion compensation. Experimental results demonstrated that the proposed method could achieve a 1.35% reduction in the BD rate on average for the luma component, compared with H.266/VVC. In the future, we will design a more effective neural network to improve the coding efficiency and lightweight neural networks to reduce the complexity, while preserving the coding efficiency.  
Figure 10: Number of CUs with different gains.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class D**} & \multicolumn{8}{c}{**Average (CNN\(-\)DCT) PSNR for Each Block**} \\ \cline{2-11}  & \(\mathbf{16\times 16}\) & \(\mathbf{16\times 32}\) & \(\mathbf{32\times 16}\) & \(\mathbf{32\times 32}\) & \(\mathbf{16\times 64}\) & \(\mathbf{64\times 16}\) & \(\mathbf{32\times 64}\) & \(\mathbf{64\times 32}\) & \(\mathbf{64\times 64}\) & **PSNR Gain** \\ \hline BasketballPass & 0.125 & 0.088 & 0.066 & 0.006 & 0.080 & 0.009 & 0.012 & 0.028 & 0.005 & 0.036 \\ \hline BQSquare & 0.175 & 0.177 & 0.179 & 0.051 & 0.203 & 0.150 & 0.068 & 0.038 & 0.055 & 0.110 \\ \hline BlowingBubbles & 0.067 & 0.050 & 0.040 & 0.011 & 0.029 & 0.028 & 0.014 & 0.008 & 0.000 & 0.030 \\ \hline RaceHorses & 0.072 & 0.047 & 0.014 & 0.003 & 0.027 & \(-0.040\) & 0.007 & \(-0.042\) & \(-0.15\) & 0.030 \\ \hline Average & 0.110 & 0.091 & 0.075 & 0.018 & 0.085 & 0.037 & 0.025 & 0.008 & \(-0.023\) & 0.052 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PSNR gain for RBENN for different CUs.  
**Author Contributions:** Conceptualization, Y.C., H.Y. and S.J.; methodology, Y.C., H.Y., S.J. and C.F.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, H.Y., S.J. and C.F.; data curation, Y.C., S.J. and C.F.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., H.Y., S.J. and C.F.; visualization, Y.C. and S.J.; supervision, S.J. and C.F.; project administration, H.Y. and S.J.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China under Grants 62222110 and 62172259, the Taishan Scholar Project of Shandong Province (tsqn202103001), the open project program of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under Grant VRLAB2021A01, the Natural Science Foundation of Shandong Province of China under Grant (ZR2022 ZD38), the Central Guidance Fund for Local Science and Technology Development of Shandong Province under Grant YDZX2021002, and the Major Scientific and Technological Innovation Project of Shandong Province under Grant 2020 CXGC010109.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.