# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments
### Experimental Results
#### 4.2.3 Comparison to State-of-the-Arts  
In this section, we compare the proposed CMGNet with state-of-the-art video quality enhancement networks, i.e., MPQE2.0 [43], STDF [44], and TDAN [60]. It is noted that TDAN was originally designed for super-resolution tasks. Since it is a typical network using the deformable convolution for frame alignment, we remove the Pixel-Shuffle layer to extend it suitable for quality enhancement for comparison with the proposed CMGNet. Moreover, all of the compared methods take the preceding frame, i.e., \(X_{t-1}\), and the succeeding frame, i.e., \(X_{t+1}\), to help enhance the quality of the target frame, i.e., \(X_{t}\), while our proposed model takes four preceding frames in the reference frame list as inputs. In addition, the compared models are all trained on camera-captured nature videos while we aim at cloud gaming videos. For a fair comparison, we modified parts of their architectures and trained them from scratch using the same game videos dataset as ours. The overall quantitative performance measured by PSNR and SSIM is shown in Table 4. We also show the parameters (sum of the luma network and the chroma network) of each model in the first row. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**QP**} & \multirow{2}{*}{**Sequences**} & \multicolumn{2}{c}{**MFQE 2.0**} & \multicolumn{2}{c}{**STDF**} & \multicolumn{2}{c}{**TDAN**} & \multicolumn{2}{c}{**CMGNet (Proposed)**} \\  & & **(408.03 K)** & \multicolumn{2}{c}{**(540.51 K)**} & \multicolumn{2}{c}{**(2.74 M)**} & \multicolumn{2}{c}{**(1.96 M)**} \\ \cline{3-10}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{22} & Secret Shop & 44.210 & 0.9487 & 44.193 & 0.9476 & 44.199 & 0.9471 & 44.214 & 0.9477 \\  & Robot Repair & 44.258 & 0.9599 & 44.352 & 0.9613 & 44.332 & 0.9612 & 44.372 & 0.9613 \\  & VRChat & 40.829 & 0.9513 & 40.864 & 0.9500 & 40.793 & 0.9492 & 40.856 & 0.9493 \\  & Average & 43.099 & **0.9533** & 43.136 & 0.9530 & 43.108 & 0.9525 & **43.147** & 0.9528 \\ \hline \multirow{4}{*}{27} & Secret Shop & 41.269 & 0.9170 & 41.245 & 0.9156 & 41.210 & 0.9124 & 41.320 & 0.9169 \\  & Robot Repair & 41.527 & 0.9383 & 41.540 & 0.9403 & 41.373 & 0.9380 & 41.606 & 0.9409 \\  & VRChat & 36.476 & 0.9026 & 36.496 & 0.9010 & 36.375 & 0.8981 & 36.518 & 0.9013 \\  & Average & 39.757 & 0.9193 & 39.760 & 0.9190 & 39.653 & 0.9162 & **39.815** & **0.9197** \\ \hline \multirow{4}{*}{32} & Secret Shop & 38.809 & 0.8751 & 38.761 & 0.8747 & 38.740 & 0.8626 & 38.815 & 0.8743 \\  & Robot Repair & 39.118 & 0.9094 & 39.185 & 0.9141 & 39.173 & 0.9133 & 39.145 & 0.9139 \\  & VRChat & 33.030 & 0.8334 & 33.044 & 0.8326 & 32.990 & 0.8300 & 33.055 & 0.8318 \\  & Average & 36.986 & 0.8726 & 36.997 & **0.8738** & 36.967 & 0.8687 & **37.005** & 0.8733 \\ \hline \multirow{4}{*}{37} & Secret Shop & 36.479 & 0.8130 & 36.410 & 0.8244 & 36.235 & 0.7825 & 36.479 & 0.8275 \\  & Robot Repair & 36.670 & 0.8615 & 36.767 & 0.8723 & 36.767 & 0.8730 & 36.806 & 0.8750 \\ \cline{1-1}  & VRChat & 30.502 & 0.7450 & 30.507 & 0.7528 & 30.505 & 0.7532 & 30.508 & 0.7534 \\ \cline{1-1}  & Average & 34.550 & 0.8065 & 34.561 & 0.8165 & 34.502 & 0.8029 & **34.598** & **0.8186** \\ \hline \multicolumn{4}{c}{Average} & 38.598 & 0.8879 & 38.614 & 0.8906 & 38.558 & 0.8851 & **38.641** & **0.8911** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison to State-of-the-arts. Figure 8: Quality enhancement difference under QP = 37. (a) \(\Delta\)PSNR. (b) \(\Delta\)SSIM. It can be seen in Table 4 that the proposed model performs the best compared to the other three models in general, where PSNR has an increase of 0.02 dB\(\sim\)0.08 dB and SSIM has an increase of 0.005\(\sim\)0.06 on average. Taking an average of PSNR under each QP, our model achieves the highest performance. Although in terms of SSIM, our model is sub-optimal at QP 22 and 32, the decrease is negligible (i.e., only about 0.0005) compared to the best performance.