## 4 Experimental Results
### Experimental Setup  
In this paper, all the algorithms are implemented with ANSI C in Microsoft Visual C++ 6.0 and MATLAB R2012b programming environments. Our experiments are conducted on a 4-core ([EMAIL_ADDRESS] GHz) computer equipped with RAM 8 GB that is also used to measure the computational \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequences** & **Resolution** & \(\mathbf{K^{1}}\) & **Characteristics** \\ \hline Football & 352 \(\times\) 288 & 260 & Fast camera and human subject motion, highly spatial details \\ Foreman & 352 \(\times\) 288 & 300 & Fast camera and content motion with pan at the end \\ Soccer & 352 \(\times\) 288 & 300 & Fast changes in motion, rapid camera panning \\ Crew & 704 \(\times\) 576 & 300 & Multiple moderate objects movement \\ Ice & 704 \(\times\) 576 & 240 & Still background and moderate human subject motion \\ City & 704 \(\times\) 576 & 300 & Fast camera motion, high detail of buildings \\ Johnny & 1280 \(\times\) 720 & 100 & Still background and low local motion \\ KristenAndSara & 1280 \(\times\) 720 & 100 & Still background and moderate local motion \\ Stockholm & 1280 \(\times\) 720 & 100 & Moderate camera panning, high detail of buildings \\ Basketball & 1920 \(\times\) 1080 & 100 & Fast camera and human subject motion, highly spatial details \\ Cactus & 1920 \(\times\) 1080 & 100 & Cirling motion and highly spatial details \\ Park\_joy & 1920 \(\times\) 1080 & 100 & Camera and content motion, high detail of trees \\ Traffic & 2560 \(\times\) 1600 & 100 & Moderate translational motion and highly spatial details \\ PeopleOnStreet & 2560 \(\times\) 1600 & 100 & Still background and many human subject motion \\ \hline \hline \end{tabular} \({}^{1}\) The number of frames in the test video sequence.  
\end{table} 
Table 3: Properties of standard video test sequences. Figure 3: The spatial and temporal information indices of the test sequences (red star represents the coordinate value of SI and TI in the test sequence). is observed that the codec with the proposed algorithm achieves the best R-D performance among all codecs. From these figures, we can see that our method is noted with average PSNR improvement over other methods about 0.53-3.2 dB. As shown in Figure 4a, for the \"Soccer\" sequence, the proposed algorithm yields 0.79, 1.15, 1.4, and 2.7 dB higher PSNR values than the ENH-MC-EZBC, RWTH-MC-EZBC, RPI-MC-EZBC, and MC-EZBC in average, respectively. Figure 4: R-D performance comparisons among five different codecs for sequences: (**a**) Soccer; (**b**) Crew; (**c**) Stockholm; (**d**) Basketball; (**e**) Park_joy, and (**f**) PeopleOnStreet. Average PSNR gains for all the test sequences are also displayed in Figure 5, one can see that the proposed Lagrange multiplier algorithm achieves an average of 0.5-3.57 dB improvement in PSNR results. The PSNR gains are more especially significant for the test sequences with complex texture and/or highly moving objects, such as \"Foreman\", \"Soccer\", \"City\", \"Stockholm\", \"Basketball\", and \"Park_joy\" sequences. These test sequences contain abrupt changes over frames in video content characteristics with fast moving objects and highly spatial details and tend to be encoded with various coding types. Thus, for all target bitrates ranges, our method shows the remarkable superiorities of RD performances with higher PSNR gains by performing content-adaptive Lagrange multiplier selection. Figure 6 shows the average PSNR of test video sequences \"Soccer\" and \"Park_joy\" at different target bitrates for various Lagrange multiplier values, where the ideal points and the points obtained by the proposed Lagrange multiplier selection are respectively marked. The observation indicates again that the proposed Lagrange multiplier results in the near-ideal rate-distortion performance for 3-D wavelet-based SVC at different target bitrates. Figure 5: The average PSNR gains obtained for various test sequences.