# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 4 Experimental Results
### Performance Analysis of Multi-Scale-Based End-to-End Rate Control  
For the multi-scale-based end-to-end rate control experiment, four RD points comprising \(\lambda\) = 256, 512, 1024, and 2048, were selected, and \(\times\)2 bicubic downsampling was used. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & \begin{tabular}{l} **Parameters** \\ **(M)** \\ \end{tabular} & \begin{tabular}{l} **FLOPs** \\ **(G)** \\ \end{tabular} & \begin{tabular}{l} **Manga109** \\ **PSNR/SSIM** \\ \end{tabular} & \begin{tabular}{l} **BSD100** \\ **PSNR/SSIM** \\ \end{tabular} & \
\begin{tabular}{l} **Set5** \\ **PSNR/SSIM** \\ \end{tabular} &
\begin{tabular}{l} **Set14** \\ **PSNR/SSIM** \\ \end{tabular} \\ \hline Scale & \(\times\)3 & & & & & & \\ Bicubic & \(\backslash\) & \(\backslash\) & 26.94/0.855 & 27.20/0.737 & 30.38/0.865 & 27.54/0.773 \\ SRCNN [40] & 0.009 & 6.1 & 30.47/0.911 & 28.40/0.786 & 32.74/0.908 & 29.29/0.820 \\ VDSR [41] & 0.667 & 70.5 & 31.87/0.918 & 29.19/0.801 & 34.48/0.921 & 30.22/0.840 \\ EDSR [42] & 1.381 & 8679 & 32.01/0.920 & 29.24/0.808 & 34.64/0.927 & 30.51/0.846 \\ RCAN [43] & 1.582 & 120 & 34.03/0.926 & 29.31/0.810 & 34.72/0.929 & 30.64/0.848 \\ HEDS-Net & 0.267 & 15.96 & 34.17/0.930 & 29.40/0.825 & 34.79/0.932 & 30.77/0.850 \\ \hline Scale & \(\times\)4 & & & & & \\ Bicubic & \(\backslash\) & \(\backslash\) & 26.39/0.760 & 25.95/0.667 & 28.41/0.810 & 26.01/0.713 \\ SRCNN [40] & 0.009 & 6.1 & 27.58/0.851 & 26.90/0.710 & 30.48/0.862 & 27.58/0.755 \\ VDSR [41] & 0.667 & 70.5 & 30.83/0.858 & 27.69/0.720 & 31.15/0.880 & 27.85/0.760 \\ EDSR [42] & 1.381 & 8679 & 31.02/0.860 & 27.75/0.721 & 31.35/0.883 & 28.01/0.768 \\ RCAN [43] & 1.582 & 120 & 31.10/0.910 & 27.84/0.742 & 32.62/0.900 & 28.85/0.787 \\ HEDS-Net & 0.267 & 15.96 & 31.13/0.912 & 27.97/0.751 & 32.69/0.901 & 28.92/0.790 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 9: Visualization map of PSNR indexes for SRCNN, VDSR, EDSR, RCAN, and HEDS-Net. We use the high-resolution test Classes for comparison, which comprises Classes A1, A2, B, and E. The accurate bit rate results are shown in Table 5.  
In Table 5, the average accuracies of DCVC, [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm are 2.67%, 3.82%, 5.69%, and 1.82%, respectively. Therefore, the proposed algorithm has the best control accuracy. Since the proposed algorithm processes the original video via the \(\times\)2 bicubic downsampling operation, reducing video data will be helpful for controlling the bit rate. On the other hand, end-to-end rate control can allocate suitable bit rates and \(\lambda\) for every frame based on the temporal coding feature; these will improve the bit rate control's accuracy. The experimental results of the PSNR and SSIM BD-rates are shown in Table 6.  
In Table 6, the BD-rate (PSNR) indexes of [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm are \(-1.09\%\), \(-0.46\%\), and \(-1.24\%\), respectively. The BD-rate SSIM indexes are \(-0.48\%\), \(-0.28\%\), and \(-0.50\%\), respectively. Therefore, the proposed algorithm has the best coding performance. Since the end-to-end rate control and HEDS-Net are trained separately, the proposed algorithm satisfies the diversity of coding features. Therefore, the proposed algorithm can improve coding performance significantly with high rate control accuracies. To measure coding time complexity, the test sequences in Table 5 are used, and 100 frames are encoded. It should be noted that the original frame of the proposed algorithm utilizes \(\times\)2 bicubic downsampling operations. The coding time comparisons of DCVC, [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm are shown in Table 7.