# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**TQA**} & \multicolumn{6}{c}{**Average Number of Searches in Various Codebooks**} \\ \cline{2-7}  & **CB1** & **CB2** & **CB11** & **CB12** & **CB13** & **CB21** & **CB22** \\ \hline
0.90 & 10.56 & 15.82 & 6.55 & 8.48 & 8.21 & 4.06 & 4.69 \\
0.91 & 10.99 & 16.26 & 6.62 & 8.56 & 8.30 & 4.06 & 4.72 \\
0.92 & 11.40 & 16.79 & 6.63 & 8.65 & 8.38 & 4.22 & 4.82 \\
0.93 & 11.86 & 17.36 & 6.73 & 8.70 & 8.49 & 4.22 & 4.90 \\
0.94 & 1A further observation in Table 6 reveals that the corresponding values of LR in CB1 and CB2 are close among various search algorithms, but exclusive of the original version of BSS-VQ, and this fact applies to the values of overall LR as well. This finding validates the argument that the search performance of BSS-VQ is affected by codeword distributions, and the adverse distribution effect can be eased using the presented search algorithm. Furthermore, the upgraded version of BBS-VQ provides an overall LR of up to 94.20% at \(TQA=0.90\), and is experimentally validated to outperform its counterparts. With the original version of BSS-VQ as a benchmark, Table 7 exclusively gives an overall search load reduction using the presented algorithm versus \(TQA\). As tabulated therein, the values of LR range between 42.05 and 51.80%, a significant outperformance over the benchmark. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LR in CB1**} & \multicolumn{2}{c}{**LR in CB2**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{1}{c}{**Overall LR**} \\  & **(\%)** & **(\%)** & **Search Load** & **(\%)** \\ \hline Full Search & Benchmark & Benchmark & 5280 & Benchmark \\ EEENNS & 77.03 & 75.05 & 1253.76 & 76.26 \\ DI-TIE & 83.42 & 83.29 & 878.88 & 83.36 \\ ITIE & 77.34 & 75.77 & 1165.99 & 77.92 \\ \hline  & 0.90 & 93.98 & 89.67 & 528.78 & 89.99 \\  & 0.91 & 93.71 & 89.25 & 548.77 & 89.61 \\  & 0.92 & 93.44 & 88.73 & 570.76 & 89.19 \\ Original & 0.93 & 93.05 & 88.19 & 595.34 & 88.72 \\ version of & 0.94 & 92.63 & 87.61 & 624.92 & 88.16 \\ BSS-VQ & 0.95 & 92.18 & 86.88 & 657.95 & 87.54 \\ (\(TQA\)) & 0.96 & 91.66 & 86.01 & 696.62 & 86.81 \\  & 0.97 & 90.95 & 85.01 & 743.15 & 85.93 \\  & 0.98 & 89.96 & 83.66 & 808.06 & 84.70 \\  & 0.99 & 88.39 & 81.60 & 902.67 & 82.90 \\ \hline  & 0.90 & 95.88 & 93.82 & 306.41 & 94.20 \\  & 0.91 & 95.71 & 93.65 & 314.19 & 94.05 \\  & 0.92 & 95.55 & 93.44 & 323.09 & 93.88 \\ Upgraded & 0.93 & 95.37 & 93.22 & 332.32 & 93.71 \\ version & 0.94 & 95.15 & 93.01 & 342.42 & 93.51 \\ (\(TQA\)) & 0.95 & 94.92 & 92.75 & 353.80 & 93.30 \\  & 0.96 & 94.66 & 92.40 & 367.20 & 93.05 \\  & 0.97 & 94.32 & 92.08 & 382.26 & 92.76 \\  & 0.98 & 93.86 & 91.60 & 404.15 & 92.35 \\  & 0.99 & 93.18 & 90.91 & 435.10 & 91.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Load reduction comparison among various algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(TQA\)} & \multicolumn{2}{c}{**BSS-VQ**} & \multirow{2}{*}{**Proposed**} & \multirow{2}{*}{**LR (\%)**} \\  & **(Benchmark)** & & & \\ \hline
0.90 & 528.78 & 306.41 & 42.05 \\
0.91 & 548.77 & 314.19 & 42.75 \\
0.92 & 570.76 & 323.09 & 43.39 \\
0.93 & 595.34 & 332.32 & 44.18 \\
0.94 & 624.92 & 342.42 & 45.21 \\
0.95 & 657.95 & 353.80 & 46.23 \\
0.96 & 696.62 & 367.20 & 47.29 \\
0.97 & 743.15 & 382.26 & 48.56 \\
0.98 & 808.06 & 404.15 & 49.99 \\
0.99 & 902.67 & 435.10 & 51.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall search load comparison between BSS-VQ and proposed algorithms. On the other hand, Table 8 presents the memory required of lookup tables for the proposed algorithm. Table 8 is divided into three portions: Storage for BSS spaces, dichotomy positions, and TIE lookup tables. The memory size of each BSS space is equal to \(BSize\) x \(CSize\) x 4 (for FLOAT data type) bytes, each dichotomy position = \(Dim\) x 4 (FLOAT data type) bytes, and each TIE tables = \(CSize\) x (\(CSize-1\)) x 5 (UINT8 + FLOAT data type).