# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Subpicture Parallel Algorithms Analysis
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**NP** & **Layout** & \multicolumn{4}{c}{**Speed-Up**} \\ \hline  & & **QP = 22** & **QP = 27** & **QP = 32** & **QP = 37** \\ \hline  & & & **Traffic (2560 \(\times\) 1600)** & \\ \hline
2P & 1 \(\times\) 2 & 1.90 & 1.87 & 1.92 & 1.97 \\  & 2 \(\times\) 1 & 1.98 & 1.93 & 1.87 & 1.97 \\
4P & 1 \(\times\) 4 & 3.79 & 3.70 & 3.72 & 3.86 \\  & 2 \(\times\) 2 & 3.86 & 3.73 & 3.77 & 3.81 \\  & 4 \(\times\) 1 & 3.64 & 3.75 & 3.77 & 3.86 \\
6P & 1 \(\times\) 6 & 5.35 & 5.50 & 5.37 & 5.37 \\  & 2 \(\times\) 3 & 5.80 & 5.66 & 5.60 & 5.76 \\  & 3 \(\times\) 2 & 5.56 & 5.40 & 5.48 & 5.59 \\  & 6 \(\times\) 1 & 5.15 & 5.31 & 5.39 & 5.55 \\
8P & 1 \(\times\) 8 & 7.11 & 6.76 & 6.60 & 6.63 \\  & 2 \(\times\) 4 & 6.74 & 7.04 & 7.06 & 7.38 \\  & 4 \(\times\) 2 & 7.00 & 7.21 & 7.34 & 7.54 \\  & 8 \(\times\) 1 & 7.36 & 7.36 & 7.42 & 7.58 \\
9P & 1 \(\times\) 9 & 7.57 & 7.23 & 7.33 & 7.56 \\  & 3 \(\times\) 3 & 7.76 & 7.63 & 7.76 & 8.02 \\  & 9 \(\times\) 1 & 7.42 & 7.28 & 7.43 & 7.78 \\
10P & 1 \(\times\) 10 & 7.43 & 7.49 & 7.57 & 7.77 \\  & 2 \(\times\) 5 & 8.39 & 8.29 & 8.47 & 8.51 \\  & 5 \(\times\) 2 & 8.87 & 8.83 & 8.80 & 9.07 \\  & 10 \(\times\) 1 & 9.07 & 8.88 & 9.11 & 9.35 \\ \hline  & & & **ParkScene (1920 \(\times\) 1080)** & \\ \hline
2P & 1 \(\times\) 2 & 1.97 & 1.89 & 1.85 & 1.85 \\  & 2 \(\times\) 1 & 1.93 & 1.81 & 1.83 & 1.87 \\
4P & 1 \(\times\) 4 & 3.56 & 3.46 & 3.40 & 3.50 \\  & 2 \(\times\) 2 & 3.72 & 3.66 & 3.62 & 3.64 \\  & 4 \(\times\) 1 & 3.50 & 3.42 & 3.44 & 3.49 \\
6P & 1 \(\times\) 6 & 5.26 & 5.14 & 5.01 & 5.14 \\  & 2 \(\times\) 3 & 5.12 & 4.99 & 4.96 & 5.15 \\  & 3 \(\times\) 2 & 5.53 & 5.33 & 5.39 & 5.45 \\  & 6 \(\times\) 1 & 5.55 & 5.46 & 5.44 & 5.53 \\
8P & 1 \(\times\) 8 & 5.72 & 6.02 & 5.98 & 5.90 \\  & 2 \(\times\) 4 & 6.70 & 6.60 & 6.57 & 6.51 \\  & 4 \(\times\) 2 & 6.78 & 6.50 & 6.65 & 6.63 \\  & 8 \(\times\) 1 & 6.77 & 6.56 & 6.58 & 6.75 \\
9P & 1 \(\times\) 9 & 7.75 & 6.91 & 7.10 & 7.43 \\  & 3 \(\times\) 3 & 7.51 & 7.17 & 7.18 & 7.34 \\  & 9 \(\times\) 1 & 6.70 & 6.58 & 6.56 & 6.60 \\
10P & 1 \(\times\) 10 & 7.70 & 7.60 & 6.47 & 7.44 \\  & 2 \(\times\) 5 & 7.60 & 7.46 & 7.48 & 7.58 \\  & 5 \(\times\) 2 & 8.86 & 8.41 & 8.56 & 8.55 \\  & 10 \(\times\) 1 & 8.81 & 8.51 & 8.31 & 8.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Speed-up evolution for high-resolution video sequences for the tile-based parallel algorithm. Regarding R/D performance, in Figure 10 we show the BD-rate results for each tile partitioning layout using the Bjontegaard method [20]. This value measures the bit rate overhead that introduces the tile-based parallel algorithm when compared with the sequential version. As can be seen, the bit rate overhead increases as the number of processes does. This is an expected result because no information from other previously encoded tiles is available to perform the intra prediction. Square-like tile partitioning layouts have a better R/D performance than the rest of layouts. This is mainly because in square-like tile layouts, every single CTU has more neighbors, which are inside the same tile, than in row-like or column-like tile layouts. Therefore, the redundancies of nearby CTUs can be exploited. We can conclude, from the information shown in Figure 10, that row-like tile layouts, i.e., layouts