# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments
### Experimental Results  
#### 4.2.1 Compression Performance of the Coding Optimization Algorithm  
We first evaluate the rate-distortion performance of our proposed algorithm to validate its efficiency. We take the commonly used H.265 as the anchor and compare our proposed algorithm with it in terms of BD-rate and BD-PSNR under the LDP configuration. Negative BD-rate values and positive BD-PSNR values indicate better performance. It should be noted that the proposed algorithm needs transmit the camera motion information to the decoder side, whereas the additional overhead is mostly below 0.02%. Hence, we ignore the bits for camera motion when calculating the two metrics. Table 1 lists the rate savings and quality gains of three test videos, where it can be observed that 4.0%, 8.51%, 2.20% bitrate savings and 0.12, 0.25, 0.07 quality gains for different test sequences can be achieved, respectively. On average, our proposed algorithm can achieve a 0.15 dB improvement relative to H.265 at the same bitrate or saves 4.91% of the bitrate to achieve the same PSNR. We also provide the RD curves in Figure 6 for better illustration. The blue lines in Figure 6 represent the performance of the traditional codec H.265 and the red ones indicate that of the proposed algorithm. It is obvious that the red line is above the blue line in each subplot, which demonstrates the superior RD performance of the proposed algorithm on different sequences. Moreover, our algorithm can achieve better RD performance at both low and high bit rates, proving the superiority of the proposed coding optimization algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequences** & **BD-Rate** & **BD-PSNR** \\ \hline Secret Shop & \(-4.03\%\) & \(0.1210\) \\ Robot Repair & \(-8.51\%\) & \(0.2540\) \\ VRChat & \(-2.20\%\) & \(0.0761\) \\ Average & \(-4.91\%\) & \(0.1503\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: BD-Rate and BD-PSNR on test videos relative to H.265.  
We also give the encoding and decoding time of the proposed algorithm in Table 2 to compare the complexity. All experiments are tested on an Intel (R) Core (TM) i7-9700K CPU @ 3.60 GHz processor and an NVIDIA GeForce RTX 2060 GPU. The forward operation of the proposed CMGNet is conducted with GPU acceleration, and the remaining operations are performed by the CPU. In Table 2, we can observe that compared with H.265/HEVC, our algorithm takes 137% of the encoding time, 9856% of the decoding time, and 157% of the total time. The proposed scheme has little increase in encoding complexity while greatly influencing the decoding time. The reasons are the long network inference time and the additional time-consuming CPU-GPU memory transfer operation. Choosing a high-performance GPU device can speed up inference time to a certain extent. Considering the complexity is positively related to parameters and floating-point operations per second (FLOPS) of the model, we can simplify the model to speed up the network forward operation in the future. High computational complexity is one of the disadvantages of CNN-based methods, and it is also an important factor affecting the implementation of the decoder. Further optimization to speed up the operation of the network is crucial and is currently under our investigation.