# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Analyzing Experimental Outcomes
#### 3.3.2 Comparison of Subjective Quality  
To assess the subjective quality, we compared the experimental results of the proposed algorithm and the one in HM16.5 on sequences with different characteristics. For example, the Class A Traffic sequence with a flat mean, the Class B Cactus and Class E Kristen & Sara sequence with a complex background, and the Class C BQ Mall sequence with complex heterogeneous texture. To make it easier to see the image loss details, we provide a local magnification area for subjective quality comparison, as shown in Figure 9. The subjective effects of the suggested method and the one in HM16.5 were almost identical. The PSNR loss of Traffic, Cactus, BQ Mall, and Kristen & Sara in the suggested method was 0.0181, 0.0239, 0.0068, and 0.0317, respectively, when compared to the methodology in HM16.5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**BD-BR**} & \multicolumn{2}{c}{**BD-**} & \multicolumn{2}{c}{\(\Delta\)\(\boldsymbol{T}\)(\%)} & \multicolumn{2}{c}{\(\Delta\)\(\boldsymbol{T}_{avg}\)} \\  & & **(\%)** & **(dB)** & **QP = 22** & **QP = 27** & **QP = 32** & **QP = 37** & **(\%)** \\ \hline People on Street & 2.11 & \(-\)0.12 & \(-\)62.59 & \(-\)68.02 & \(-\)65.43 & \(-\)64.56 & \(-\)65.15 \\ Traffic & 2.47 & \(-\)0.13 & \(-\)67.50 & \(-\)66.77 & \(-\)69.64 & \(-\)73.14 & \(-\)69.26 \\ Average Class A & 2.29 & \(-\)0.13 & \(-\)65.05 & \(-\)67.40 & \(-\)67.54 & \(-\)68.85 & \(-\)67.21 \\ \hline Basketball Drive & 3.86 & \(-\)0.09 & \(-\)71.37 & \(-\)77.46 & \(-\)80.00 & \(-\)82.38 & \(-\)77.80 \\ BQ Terrace & 1.37 & \(-\)0.09 & \(-\)65.25 & \(-\)64.76 & \(-\)74.41 & \(-\)67.51 & \(-\)67.98 \\ Cactus & 2.02 & \(-\)0.05 & \(-\)60.66 & \(-\)64.91 & \(-\)68.34 & \(-\)71.48 & \(-\)66.35 \\ Kimono & 1.47 & \(-\)0.05 & \(-\)83.50 & \(-\)85.35 & \(-\)89.99 & \(-\)90.42 & \(-\)87.32 \\ Park Scene & 2.15 & \(-\)0.09 & \(-\)74.99 & \(-\)67.27 & \(-\)70.63 & \(-\)74.17 & \(-\)71.77 \\ Average Class B & 2.17 & \(-\)0.07 & \(-\)71.15 & \(-\)71.95 & \(-\)76.67 & \(-\)77.19 & \(-\)74.24 \\ \hline Basketball Drill & 2.94 & \(-\)0.14 & \(-\)46.77 & \(-\)51.38 & \(-\)68.23 & \(-\)74.97 & \(-\)60.34 \\ Party Scene & 0.86 & \(-\)0.07 & \(-\)42.50 & \(-\)40.93 & \(-\)42.54 & \(-\)45.13 & \(-\)42.78 \\ Race Horses C & 1.64 & \(-\)0.10 & \(-\)54.81 & \(-\)54.63 & \(-\)57.61 & \(-\)62.58 & \(-\)56.51 \\ BQ Mall & 1.18 & \(-\)0.07 & \(-\)46.86 & \(-\)50.37 & \(-\)53.94 & \(-\)58.06 & \(-\)52.33 \\ Average Class C & 1.65 & \(-\)0.10 & \(-\)47.53 & \(-\)49.10 & \(-\)55.35 & \(-\)59.97 & \(-\)52.99 \\ \hline Basketball Pass & 1.96 & \(-\)0.11 & \(-\)62.21 & \(-\)72.57 & \(-\)75.99 & \(-\)78.17 & \(-\)72.24 \\ Blowing Bubbles & 0.74 & \(-\)0.04 & \(-\)37.07 & \(-\)40.11 & \(-\)45.55 & \(-\)51.86 & \(-\)43.76 \\ BQ Square & 0.24 & \(-\)0.02 & \(-\)64.49 & \(-\)44.78 & \(-\)46.21 & \(-\)47.60 & \(-\)50.77 \\ Race Horses & 1.04 & \(-\)0.07 & \(-\)45.57 & \(-\)47.00 & \(-\)49.34 & \(-\)53.55 & \(-\)48.87 \\ Average Class D & 1.00 & \(-\)0.06 & \(-\)52.44 & \(-\)51.18 & \(-\)54.20 & \(-\)57.81 & \(-\)53.91 \\ \hline Four People & 2.61 & \(-\)0.15 & \(-\)68.61 & \(-\)75.95 & \(-\)66.71 & \(-\)76.08 & \(-\)71.84 \\ Johnny & 3.39 & \(-\)0.14 & \(-\)71.93 & \(-\)74.21 & \(-\)76.60 & \(-\)78.31 & \(-\)75.26 \\ [PERSON] \& [PERSON] & 2.82 & \(-\)0.14 & \(-\)70.09 & \(-\)71.45 & \(-\)73.58 & \(-\)75.15 & \(-\)72.57 \\ Average Class E & 2.94 & \(-\)0.14 & \(-\)70.21 & \(-\)73.87 & \(-\)72.30 & \(-\)76.51 & \(-\)73.22 \\ \hline Average & 1.94 & \(-\)0.09 & \(-\)60.91 & \(-\)62.07 & \(-\)65.20 & \(-\)68.02 & \(-\)64.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for the A–E sequences. Thus, the method proposed has an excellent performance in both the image quality and coding efficiency. Moreover, to further demonstrate why the suggested method is of low complexity, we compared the CU partition results of the proposed algorithm to that of HM16.5. Figure (a)a,b show a zoomed-in demonstration of the two algorithms in specific regions (r1-r6).