# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 4 Experimental Results
### Performance Analysis of HEDS-Net  
For training HEDS-Net, 800 images in DIV2K [36] are used. The low-resolution image is obtained via bicubic downsampling, and the downsampling rates are set as \(\times 2\), \(\times 3\), and \(\times 4\). To enhance the diversity of the training set, the images in the train set are rotated 90 degrees, 180 degrees, and 270 degrees. The cosine annealing method is used to decrease the learning rate automatically. The initial learning rate is set as \(1\times 10^{-3}\). After \(1\times 10^{5}\) iterations, the learning rate decreases to \(1\times 10^{-7}\) gradually. PSNR and SSIM are used to evaluate the difference between the HEDS-Net output and the original image. The datasets of Manga109 [37], BSD100 [38], Set5 [39], and Set14 [40] are used for testing. The algorithms of Bicubic, SRCNN [41], VDSR [42], EDSR [43], and RCAN [44] are used for comparison. The results are shown in Table 4.  
\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 & \(\times 2\) & & & & & \\ Bicubic & \(\backslash\) & & 30.79/0.932 & 29.56/0.843 & 33.65/0.928 & 30.23/0.867 \\ SRCNN [40] & 0.009 & 6.1 & 35.59/0.965 & 31.35/0.887 & 36.65/0.953 & 32.44/0.905 \\ VDSR [41] & 0.667 & 70.5 & 38.87/0.972 & 32.09/0.898 & 37.89/0.955 & 33.69/0.912 \\ EDSR [42] & 1.381 & 8679 & 39.49/0.978 & 32.31/0.901 & 38.10/0.959 & 33.91/0.918 \\ RCAN [43] & 1.582 & 120 & 39.43/0.977 & 32.40/0.903 & 38.26/0.960 & 34.11/0.921 \\ HEDS-Net & 0.267 & 15.96 & 39.51/0.979 & 32.54/0.907 & 38.40/0.966 & 34.39/0.929 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of bicubic, SRCNN, VDSR, EDSR, RCAN, and HEDS-Net.  
Figure 8: RD curve comparisons of (A): the proposed algorithm, (B): DCVC, (C): [PERSON] et al. [22] and (D): [PERSON] et al. [9].  
In Table 4, it can be observed that HEDS-Net has better PSNR and SSIM indexes at different scales compared to other algorithms. All algorithms in the \(\times\)2 scale have the best restoration performance, and the restoration performance in the \(\times\)4 scale is the worst. Since the low-resolution image with \(\times\)4 scale downsampling loses most of the original image's detailed features, restoring the original image is more difficult compared to the \(\times\)2 scale downsampling and \(\times\)3 scale downsampling images; that is, the \(\times\)2 scale downsampling image reserves more detailed information, and the different features of this high-resolution image can be restored more easily than the \(\times\)3 scale and \(\times\)4 scale downsampling images. The parameter of HEDS-Net is 0.267M, and the complexity of HEDS-Net is measured via floating-point operations (FLOPs), which is 15.96G. It can be observed in Table 3 that the parameters and complexity of HEDS-Net are slightly greater than SRCNN, which means HEDS-Net is a lightweight network, but it achieves good restoration performance.  
The visualization map of the PSNR indexes for Set5 at the \(\times\)4 scale is shown in Figure 9. From Figure 9, it can be seen that HEDS-Net has the best restoration performance with low complexity and a few parameters. This also verifies the high computational efficiency of HEDS-Net.