# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 8 Results and Discussion

Table 7: Processing time of QCIF video sequences with QP = 37. Figure 23: Comparison of processing time with HM for QCIF video sequences using QP = 37. Figure 24: Comparison of processing time with HM for CIF video sequences using QP = 37. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Sequence** & **HM (Sec)** & **PEDBF (Sec)** \\ \hline Akiyo & 361.664 & 139.577545 \\ \hline Coastguard & 323.229 & 141.569951 \\ \hline Foreman & 293.987 & 161.030763 \\ \hline Mobile Calendar & 461.949 & 225.603688 \\ \hline Hall & 280.68 & 142.62714 \\ \hline Tempete & 200.476 & 112.878238 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Processing time of CIF video sequences with QP = 37. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Sequence** & **HM (dB)** & **PEDBF (dB)** \\ \hline Akiyo & 42.2861 & 42.332459 \\ \hline Coastguard & 38.6598 & 40.411503 \\ \hline Foreman & 39.2529 & 40.130872 \\ \hline Mobile Calendar & 37.1378 & 37.29197 \\ \hline Hall & 40.1375 & 40.525791 \\ \hline Tempete & 38.0572 & 38.495716 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _PSNR_ of CIF video sequences with QP = 27. Figure 26: Comparison of _PSNR_ with HM for CIF video sequences using QP = 27. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Sequence** & **HM (dB)** & **PEDBF (dB)** \\ \hline Akiyo & 40.8999 & 40.891213 \\ \hline Coastguard & 38.1673 & 39.927605 \\ \hline Foreman & 38.9294 & 39.793245 \\ \hline Mobile Calendar & 36.6292 & 36.744202 \\ \hline Hall & 39.8733 & 40.164219 \\ \hline Carphone & 39.6617 & 39.917189 \\ \hline Miss America & 41.5719 & 41.766507 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _PSNR_ of QCIF video sequences with QP = 27. Figure 25: Comparison of _PSNR_ with HM for QCIF video sequences using QP = 27. Based on the experiments carried out with different video sequences of different resolutions for different QP values, it is identified that as the QP value increases, the quality decreases and results in lower _PSNR_ values. In addition, the execution time decreases as \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Sequence** & **HM (Sec)** & **PEDBF (Sec)** \\ \hline Akiyo & 66.61 & 32.722607 \\ \hline Coastguard & 72.627 & 39.74846 \\ \hline Foreman & 81.681 & 41.068033 \\ \hline Mobile Calendar & 120.716 & 58.555709 \\ \hline Hall & 67.687 & 35.016408 \\ \hline Carphone & 95.34 & 55.400211 \\ \hline Miss America & 24.741 & 13.174376 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Processing time of QCIF video sequences with QP = 27. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Sequence** & **HM (Sec)** & **PEDBF (Sec)** \\ \hline Akiyo & 205.041 & 100.410937 \\ \hline Coastguard & 277.788 & 150.710927 \\ \hline Foreman & 250.539 & 126.669947 \\ \hline Mobile Calendar & 352.151 & 208.336164 \\ \hline Hall & 228.818 & 117.526938 \\ \hline Tempete & 263.123 & 156.781587 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Processing time of CIF video sequences with QP = 27. Figure 27: Comparison of processing time with HM for QCIF video sequences using QP = 27. Figure 28: Comparison of processing time with HM for CIF video sequences using QP = 27.  
the QP value increases. This behavior is as expected with any video coding algorithm. Hence, the PEDBF architecture complies with the coding standards. The execution time of the PEDBF architecture is reduced to almost half of the execution time of the HM, owing to the fact of filtering two edges in parallel.