## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.3 Decoding-Complexity Reduction and the Impact on Video Quality  
The Table 3 demonstrates the average decoding-complexity reductions and the corresponding quality impact, in PSNR, for the proposed and state-of-the-art algorithms. The algorithms proposed by [PERSON] et al. [20] and [PERSON] et al. [46] both achieved decoding-complexity reductions in the range of 10% and 20%, respectively. However, it was observed that those were achieved at the expense of a significant reduction in PSNR for a given bit rate. For example, although [PERSON] et al. [46] uses a decoding-complexity estimation model [58; 59; 60], the bit rate, decoding-complexity trade-off factors are selected independently; thus, the impact on each other is overlooked during the coding mode selection. Furthermore, only the bit rate trade-off factor [47; 49] is content-adaptive, and the decoding-complexity trade-off factor remains agnostic to the dynamics of the video sequence, which ultimately results in a higher quality loss. Similarly, the method proposed in [20] uses predefined trade-off factors and decoding-complexity-aware coding mode selection only at the PU level. Thus, the sacrifice made in video quality to maintain the bit rate requirement is greater, and performance is inferior to [PERSON] et al. [46]. These results are illustrated graphically in the \(\Delta\)PSNR vs. decoding-complexity graphs presented in the Figure 4. Here, it can be observed that both these algorithms would experience a higher quality impact in a rate-controlled scenario if they were to achieve a particular decoding-complexity. However, it should be noted that the encoding algorithm proposed by [PERSON] et al. has shown improvements in very low-in-complexity video sequences, such as \"band,\" \"cafe,\" \"poznan st.,\" etc. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Proposed L1**} & \multicolumn{2}{c}{**Proposed L2**} \\ \hline  & \(R_{e}\) \% & \(C_{e}\) \% & \(R_{e}\) \% & \(C_{e}\) \% \\ \hline Band & \(0.638\) & \(-1.348\) & \(0.350\) & \(3.395\) \\ Beergarden & \(0.383\) & \(0.733\) & \(0.189\) & \(6.716\) \\ Cafe & \(0.012\) & \(-0.750\) & \(0.010\) & \(4.248\) \\ Dancer & \(1.534\) & \(8.485\) & \(1.448\) & \(1.448\) \\ GTFly & \(0.001\) & \(-5.864\) & \(0.018\) & \(-1.326\) \\ Kimono & \(0.015\) & \(-9.019\) & \(0.014\) & \(-4.276\) \\ Musicians & \(0.992\) & \(-6.867\) & \(1.125\) & \(-2.950\) \\ Parkscene & \(0.208\) & \(-7.407\) & \(0.105\) & \(-3.340\) \\ Poznan St. & \(1.140\) & \(1.973\) & \(0.359\) & \(8.265\) \\ \hline
**Average** & \(0.54\) & \(-2.22\) & \(0.40\) & \(1.35\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decoding-complexity-controlling performance of the proposed encoding algorithm. Figure 3: An illustration of the frame-wise percentage error between the target decoding-complexity and the achieved decoding-complexity for the proposed algorithm at two complexity levels for a given bit rate (2 Mbps) for (**a**) Parkscene, (**b**) Dancer and (**c**) Musicians test sequences. The approach by [PERSON] et al. [11] modifies the decoding operations to reduce the decoding-complexity. For example, the skipping of in-loop filtering and simplifying the motion compensation operations within the decoder results in a significant complexity reduction. (It should be noted that the presented results correspond to the highest complexity reduction that can be achieved by applying the decoder modifications proposed in [11] to all frames in the bit stream). However, changing the motion compensation filters and thereby applying the decoded residuals on a predicted PU which is different from that of the encoder's, causes more distortions in the reconstructed block. Although the intra-frames that appear within the given intervals avoid the propagation of these errors, the algorithm results in a much larger PSNR reduction (cf. Figure 4). In contrast, the proposed algorithm uses a more comprehensive and dynamic approach to simultaneously control both decoding-complexity and bit rate.