# Reliability-Based View Synthesis for Free Viewpoint Video
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**Camera Set**} & \multirow{2}{*}{**Baseline (cm)**} & \multicolumn{4}{c}{**PSNR (dB)**} \\ \cline{3-6}  & & & **VSRS [26]** & **GMM-Based [5]** & **Previous [27]** & **Proposed** \\ \hline \multirow{4}{*}{_Ballet_} & \(3,5\to 4\) & 20, 20 & 29.39 & 32.29 & 28.97 & 33.56 \\  & \(1,7\to 3\) & 40, 80 & 22.64 & 31.01 & 25.23 & 32.43 \\  & \(1,7\to 4\) & 60, 60 & 22.03 & 31.13 & 25.78 & 32.54 \\  & \(1,7\to 5\) & 80, 40 & 22.12 & 30.98 & 25.12 & 32.32 \\ \hline \multirow{4}{*}{_Breakdancers_} & \(1,3\to 2\) & 20, 20 & 30.88 & 34.42 & 29.46 & 35.37 \\  & \(2,6\to 3\) & 20, 60 & 23.89 & 32.31 & 27.51 & 33.65 \\ \cline{1-1}  & \(2,6\to 4\) & 40, 40 & 23.76 & 32.66 & 27.87 & 34.41 \\ \cline{1-1}  & \(2,6\to 5\) & 60, 20 & 23.64 & 32.53 & 27.76 & 34.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average peak signal-noise ratio (PSNR) comparison of the proposed technique and three state-of-the-art techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**Camera Set**} & \multirow{2}{*}{**Baseline (cm)**} & \multicolumn{4}{c}{**SSIM**} \\ \cline{3-6}  & & & **VSRS [26]** & **GMM-Based [5]** & **Previous [27]** & **Proposed** \\ \hline \multirow{4}{*}{_Ballet_} & \(3,5\to 4\) & 20, 20 & 0.8229 & 0.8839 & 0.8114 & 0.8937 \\  & \(1,7\to 3\) & 40, 80 & 0.7976 & 0.8839 & 0.8645 & 0.8941 \\  & \(1,7\to 4\) & 60, 60 & 0.7997 & 0.8843 & 0.8688 & 0.8946 \\  & \(1,7\to 5\) & 80, 40 & 0.7913 & 0.8847 & 0.8698 & 0.8955 \\ \hline \multirow{4}{*}{_Breakdancers_} & \(1,3\to 2\) & 20, 20 & 0.8387 & 0.8687 & 0.8344 & 0.8813 \\  & \(2,6\to 3\) & 20, 60 & 0.8143 & 0.8601 & 0.8714 & 0.8872 \\ \cline{1-1}  & \(2,6\to 4\) & 40, 40 & 0.8156 & 0.8587 & 0.8702 & 0.8818 \\ \cline{1-1}  & \(2,6\to 5\) & 60, 20 & 0.8132 & 0.8565 & 0.8707 & 0.8821 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average structural similarity index (SSIM) comparison of the proposed technique and three state-of-the-art techniques. Frame-by-frame comparisons of PSNR and SSIM are shown in Figure 6. Figure 6a,b show a synthesis result with a large baseline: viewpoint _cam03_ is interpolated by _cam01_ and _cam 07_. Another PSNR and SSIM comparison (Figure 6c,d) comes from a small baseline; two reference viewpoints, _cam03_ and _cam05_, were utilized to render target virtual view _cam04_. Both instances are from the sequence _Ballet_. Obviously, exploring temporal correlations to fill the disocclusions yields better performance beyond the inpainting-based view synthesis method, which only explores the spatial correlation, especially when the baseline is large. In all the frames, our proposed framework shows more stable output than the GMM-based method. In this article, we additionally tested the computation time for all the four approaches. Greater improvements in subjective and objective image quality are brought by much more complex computation. In our proposed method, 3D warping process is performed six times and adaptive background modeling is applied twice, that is the reason why the computation cost of my proposed method is high. The first reason is that the GPU-accelerated algorithm is commonly used for image processing and the hardware performance is growing rapidly, the increased computation time for one frame will not increase too much time for synthesizing the whole sequence if parallel algorithm is adopted. The second reason is that due to the mechanism of our proposed approach, we mainly explore the contribution of depth refinement technique and adaptive background modeling, the time can be shortened if this method is applied in practical applications. After all, our proposed method is implemented using OpenCV library, the computation time is capable to reduce a lot if we carefully using coding skills.