## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.5 Impact of the Proposed Encoding Framework on Different Decoders and CPU Architectures
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**Proposed L2**} & \multicolumn{3}{c}{**Proposed L2**} & \multicolumn{3}{c}{**[PERSON] et al. [20]**} & \multirow{2}{*}{**[PERSON] et al. [34]**} & \multirow{2}{*}{**[PERSON] et al. [46]**} \\  & \multicolumn{3}{c}{**(Model Only)**} & & \multicolumn{3}{c}{**(Model + LF [34])**} & \multicolumn{3}{c}{**(**PUM + DBK)**} & \multicolumn{3}{c}{**(**MC + LF)**} & \\ \cline{2-19}  & \(\overline{\mathbf{\Lambda}\Gamma}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{\Gamma}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) & \(\widehat{\mathbf{\Lambda}\mathbf{E}\ ^{\dagger}}\) \\ \hline Band & 10.97 & 1.75 & 6.30 & 12.30 & 2.56 & 5.66 & 15.45 & 2.52 & 7.58 & 5.08 & 0.78 & 1.82 & 6.44 & 0.65 & 2.11 \\ Beergarden & 5.65 & 1.76 & 2.96 & 7.03 & 1.38 & 2.65 & 2.37 & -0.04 & 0.62 & 6.20 & 1.07 & 2.15 & 3.92 & 0.05 & 1.43 \\ Cafe & 4.15 & 3.12 & 6.27 & 4.79 & 3.08 & 4.45 & 13.5 & 10 & 13.23 & 8.24 & 3.78 & 6.52 & 3.63 & 2.13 & 3.01 \\ Dancer & 8.23 & 1.11 & 2.66 & 8.92 & 2.06 & 3.07 & 5.60 & 0.87 & 1.97 & 5.65 & 1.01 & 1.70 & 3.70 & 0.61 & 0.98 \\ GTFly & 8.76 & 3.65 & 6.08 & 10.39 & 3.92 & 5.87 & 9.25 & 2.88 & 4.93 & 5.63 & 1.71 & 2.33 & 4.46 & 1.21 & 1.80 \\ Kimono & 16.49 & 4.29 & 10.52 & 19.23 & 4.62 & 9.85 & 10.97 & 4.62 & 6.67 & 6.84 & 2.11 & 2.70 & 6.84 & 1.29 & 2.64 \\ Musicians & 16.03 & 2.04 & 6.05 & 20.23 & 3.50 & 5.91 & 6.77 & 0.68 & 2.23 & 4.64 & 0.25 & 0.81 & 4.15 & 0.42 & 0.88 \\ Parkscene & 17.71 & 3.97 & 7.02 & 17.31 & 3.04 & 5.38 & 6.42 & 0.83 & 1.73 & 9.35 & 1.78 & 2.95 & 4.88 & 0.69 & 1.50 \\ Poznan St. & 2.96 & 3.28 & 3.70 & 4.00 & 2.06 & 2.16 & 4.41 & 1.06 & 4.01 & 8.62 & 2.50 & 3.93 & 4.013 & 1.41 & 2.50 \\ \hline Average & **10.11** & **2.77** & **5.73** & **11.58** & **2.91** & **5.00** & **8.30** & **2.60** & **4.77** & **6.69** & **1.66** & **2.77** & **4.67** & **0.94** & **1.87** \\ \hline \hline \end{tabular}  
* The metrics \(\widehat{\Delta\Gamma}\) (%/dB) and \(\widehat{\Delta E}\) (%/dB) are both measured in terms of the \(\Delta\Gamma\)(%) and \(\Delta E\)(%) achieved per 1 dB PSNR quality loss for the proposed and state-of-the-art algorithms.* \(\widehat{\Delta E}\) (%/dB) achieved when using Linux ondemand frequency governor. \(\ddagger\widehat{\Delta E}\) (%/dB) achieved when using an application-specific DVFS algorithm as the frequency governor.  
\end{table} 
Table 5: Decoding-complexity and energy reduction per 1 dB quality loss for the proposed and state-of-the-art algorithms.