# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments
### Experimental Results
#### 4.2.2 Quality Enhancement of the Proposed CMGNet  
In this section, we evaluate the quality enhancement performance of our proposed CMGNet in terms of PSNR and SSIM, which represent the degree of distortion and structure similarity between the enhanced frames and the original ones, respectively. Since our goal is to generate a high-matching and high-quality reference frame to update the reference frame list, the generated reference frame is supposed to be closer to the original one than that directly generated by H.265. Therefore, we compare the quality of the generated enhanced frame with that of the unenhanced frame compressed by H.265. Table 3 presents the results averaged over 500 frames for each test sequence at four different QPs. As shown in this table, our proposed CMGNet outperforms H.265 consistently in terms of PSNR and SSIM. Specifically, with QP equal to 22, the proposed network can achieve a PSNR gain of about 0.5 dB on average over the three test sequences. Even at QP 37, we can still obtain about a 0.3 dB performance gain. The same superiority is achieved in terms of SSIM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequences** & \(\mathbf{\Delta T}_{Enc}\) & \(\mathbf{\Delta T}_{Dec}\) & \(\mathbf{\Delta T}_{Total}\) \\ \hline Secret Shop & 142\% & 11,023\% & 164\% \\ Robot Repair & 140\% & 11,796\% & 163\% \\ VRChat & 130\% & 6747\% & 144\% \\ Avreage & 137\% & 9856\% & 157\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Encoding and decoding complexity. Figure 6: Rate-distortion curves of three test videos. (**a**) Secret Shop. (**b**) Robot Repair. (**c**) VRChat. Meanwhile, our algorithm exhibits different degrees of performance gains under different test sequences, showing a good generalization enhancement effect. We also provide qualitative evaluations in Figure 7 to demonstrate the visual performance of our model. Taking QP equal to 37 as an example, three patches from different sequences are enlarged and presented below. We recommend further zooming in on the figure for a more intuitive observation. Compared to the raw frame, the unenhanced frame compressed by H.265 inevitably loses a lot of details at such a high QP. For instance, the appearance outline of the sound in the second picture becomes extremely blurred, especially around the oval-shaped loudspeaker. The enhanced frame generated by the proposed CMGNet looks smoother with fewer artifacts due to the recovery of details. It is the same in other pictures. Although the quality of the enhanced frame is not as good as the original frame, it has been demonstrated in the previous section that the enhanced frame is sufficient to optimize the traditional codec. To further analyze the impact of the camera motion in our network, we present the quality enhancement difference between the full model and that without camera motion in Figure 8, taking the QP equal to 37 as an example. It can be clearly seen from Figure 8 that the camera motion information plays a positive role in the quality enhancement, as the full model shows better performance than the model without motion information on both PSNR and SSIM metrics in the three test videos.