# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Subpicture Parallel Algorithms Analysis  
First, we will analyze both the parallel and the coding performance of the parallel algorithm based on slice partitioning. As stated in Section 2, slice partitioning may slightly limit the parallel efficiency that can be achieved. Regarding the coding performance, in most cases, the horizontal borders inserted by the slice partitioning cause a reduction in the prediction searching area of up to 75%. Moreover, each slice inserts a data header in the bitstream, which reduces the R/D performance. In Table 3, the speed-up results for the slice-based parallel algorithm are shown. As can be seen, good speed-up values are achieved, but the parallel efficiency slightly decreases as the number of processes increases. Note that, when the searching area is reduced for most of the CTUs that belong to a slice, the computational load associated with that slice decreases. As shown in Figure 5, the computational load for the first slice remains unaltered. The difference, in computational load, increases as the size of slices decreases, i.e., as the number of processes increases. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Speed-Up (QPs)**} \\ \hline
**NP** & **QP = 37** & **QP = 32** & **QP = 27** & **QP = 22** \\ \hline \multicolumn{4}{c}{**Traffic**} \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Speed-up for the slice-based parallel algorithm. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Acronym** & **Video** & **Resolution** & **Frame Rate** & **Total Number** \\  & **Sequence** & **(Pixels)** & **(Hz.)** & **of Frames** \\ \hline TRAFFI & Traffic & 2560 \(\times\) 1600 & 30 & 150 \\ PEOPON & PeopleOnStreet & 2560 \(\times\) 1600 & 30 & 150 \\ PARKSC & ParkScene & 1920 \(\times\) 1080 & 24 & 240 \\ TENNIS & Tennis & 1920 \(\times\) 1080 & 24 & 240 \\ FOURPE & FourPeople & 1280 \(\times\) 720 & 60 & 600 \\ KRI&SA & Kristen\&Sara & 1280 \(\times\) 720 & 60 & 600 \\ PARTSC & PartyScene & 832 \(\times\) 480 & 50 & 500 \\ BASKDR & BasketballDrill & 832 \(\times\) 480 & 50 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test video sequences. Table 3 presents the coding performance in PSNR and bit rate for the slice-based parallel algorithm. As expected, the PSNR values worsen as the number of slices increases. The inclusion of a data header per slice has a variable impact on the bit rate depending on the number of processes, the video resolution, and the compression rate. As the size of the slice header is fixed, the bit rate increment becomes greater as the number of processes increases. Additionally, for the configurations which produce low bit rates (low resolution and high compression rates), the percentage of bit rate increase is greater than for the rest of configurations. As shown in Table 3, the worst case produces a 10.68% of bit rate increment for PartyScene video sequence (the lowest resolution video sequence tested), 10 processes (the maximum number of processes used), and a QP value of 37 (the highest compression rate evaluated).