# 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
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**[PERSON] et al. [22]**} & \multicolumn{2}{c}{**[PERSON] et al. [9]**} & \multicolumn{2}{c}{**Proposed**} \\  & & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** \\  & & **(PSNR)** & **(SSIM)** & **(PSNR)** & **(SSIM)** & **(PSNR)** & **(SSIM)** \\ \hline \multirow{4}{*}{A1} & Tango2 & \(-3.59\%\) & \(-0.77\%\) & \(-0.20\%\) & \(-0.03\%\) & \(-0.42\%\) & \(-0.27\%\) \\  & FoodMarket4 & \(0.78\%\) & \(0.10\%\) & \(0.08\%\) & \(0.02\%\) & \(0.74\%\) & \(0.07\%\) \\  & Campfire & \(-2.01\%\) & \(-0.65\%\) & \(-0.32\%\) & \(-0.20\%\) & \(-1.66\%\) & \(-0.42\%\) \\ \hline \multirow{4}{*}{A2} & CatRobot1 & \(-2.37\%\) & \(-0.68\%\) & \(-0.32\%\) & \(-0.03\%\) & \(-0.63\%\) & \(-0.40\%\) \\  & DaylightRoad2 & \(-0.92\%\) & \(-0.12\%\) & \(-0.22\%\) & \(-0.06\%\) & \(-0.07\%\) & \(-0.12\%\) \\  & ParkRunning3 & \(0.43\%\) & \(0.04\%\) & \(0.34\%\) & \(0.05\%\) & \(0.29\%\) & \(-0.05\%\) \\ \hline \multirow{4}{*}{B} & MarketPlace & \(-0.13\%\) & \(-0.15\%\) & \(-0.64\%\) & \(-0.17\%\) & \(-2.48\%\) & \(-0.87\%\) \\  & RitualDance & \(-1.83\%\) & \(-0.58\%\) & \(-1.60\%\) & \(-0.51\%\) & \(-2.18\%\) & \(-0.78\%\) \\  & Cactus & \(-1.39\%\) & \(-0.46\%\) & \(-1.18\%\) & \(-0.33\%\) & \(-2.01\%\) & \(-0.71\%\) \\  & BasketballDrive & \(-0.42\%\) & \(-0.95\%\) & \(-0.35\%\) & \(-0.82\%\) & \(-3.44\%\) & \(-0.92\%\) \\  & BQTerrace & \(-0.73\%\) & \(-0.87\%\) & \(-0.34\%\) & \(-0.80\%\) & \(-0.80\%\) & \(-0.90\%\) \\ \hline \multirow{4}{*}{E} & FourPeople & \(-1.30\%\) & \(-0.60\%\) & \(-0.47\%\) & \(-0.38\%\) & \(-1.67\%\) & \(-0.57\%\) \\  & Johnny & \(-0.33\%\) & \(-0.49\%\) & \(-0.95\%\) & \(-0.25\%\) & \(-1.31\%\) & \(-0.53\%\) \\  & KristenAndSara & \(-1.49\%\) & \(-0.55\%\) & \(-0.30\%\) & \(-0.36\%\) & \(-1.70\%\) & \(-0.58\%\) \\ \hline
**Ave** & \(-\mathbf{1.09\%}\) & \(-\mathbf{0.48\%}\) & \(-\mathbf{0.46\%}\) & \(-\mathbf{0.28\%}\) & \(-\mathbf{1.24\%}\) & \(-\mathbf{0.50\%}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coding performance comparisons of DCVC, [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **DCVC** & **[PERSON] et al. [22]** & **[PERSON] et al. [9]** & **Proposed** \\ \hline A1 & \(3.87\%\) & \(4.41\%\) & \(7.30\%\) & \(2.01\%\) \\ A2 & \(3.43\%\) & \(5.38\%\) & \(7.56\%\) & \(2.11\%\) \\ B & \(1.69\%\) & \(2.13\%\) & \(2.91\%\) & \(1.72\%\) \\ E & \(1.67\%\) & \(3.35\%\) & \(4.97\%\) & \(1.42\%\) \\
**Average** & \(\mathbf{2.67\%}\) & \(\mathbf{3.82\%}\) & \(\mathbf{5.69\%}\) & \(\mathbf{1.82\%}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bit rate accuracy comparisons of DCVC, [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm. In Table 7, the coding time of DCVC is used as the anchor. The coding time indexes of [PERSON] et al. [22], [PERSON] et al. [9], and the proposed algorithm are 126%, 111%, and 98%, respectively. Because [PERSON] et al. [22] increased the coding complexity of the end-to-end coding model, more time was used with respect to DCVC. For [PERSON] et al. [9], the hybrid coding framework uses a substantial amount of time on prediction coding. Therefore, [PERSON] et al. [9] required more coding time compared to DCVC. For the proposed algorithm, because the \(\times\)2 bicubic downsampling operation of the original frame is used first, a substantial decrease in the amount of coding data occurs. Even though some types of neural networks are used in the proposed algorithm, which will increase coding times, data reduction will affect coding times more. Subjective comparisons of BasketballDrive and Cactus are shown in Figure 10.