# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 4 Experimental Results
### Compression Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{**G-PCC PLT [17]**} & \multicolumn{4}{c}{**G-PCC RAHT [17]**} & \multicolumn{4}{c}{**BAAC [18]**} \\ \cline{3-11}  & & & **Y** & **U** & **V** & **Y** & **U** & **V** & **Y** & **U** & **V** \\ \hline \multirow{10}{*}{Solid} & Basketball & \(-11.95\%\) & \(-13.73\%\) & \(-32.23\%\) & \(-25.25\%\) & \(-10.36\%\) & \(-24.22\%\) & \(-3.08\%\) & \(-3.11\%\) & \(-3.91\%\) \\  & Dancer & \(-14.68\%\) & \(-12.37\%\) & \(-29.03\%\) & \(-22.80\%\) & \(-15.10\%\) & \(-26.73\%\) & \(-2.70\%\) & \(-2.74\%\) & \(-3.24\%\) \\  & Longdress & \(-12.37\%\) & \(-15.28\%\) & \(-13.87\%\) & \(-17.59\%\) & \(-12.86\%\) & \(-12.62\%\) & \(-6.69\%\) & \(-7.97\%\) & \(-7.80\%\) \\  & Loot & \(-24.58\%\) & \(-68.73\%\) & \(-53.06\%\) & \(-36.50\%\) & \(-58.13\%\) & \(-52.70\%\) & \(-8.98\%\) & \(-12.79\%\) & \(-11.95\%\) \\  & Redandblack & \(-16.40\%\) & \(-21.87\%\) & \(-19.32\%\) & \(-24.89\%\) & \(-19.02\%\) & \(-18.30\%\) & \(-6.53\%\) & \(-7.25\%\) & \(-6.95\%\) \\  & Soldier & \(-17.45\%\) & \(-45.58\%\) & \(-38.75\%\) & \(-36.86\%\) & \(-55.31\%\) & \(-47.56\%\) & \(-8.43\%\) & \(-11.97\%\) & \(-11.98\%\) \\  & Thaidancer\_v & \(-18.13\%\) & \(-31.55\%\) & \(-31.42\%\) & \(-34.21\%\) & \(-33.40\%\) & \(-34.17\%\) & \(-24.23\%\) & \(-27.91\%\) & \(-28.47\%\) \\  & Queen & \(-14.24\%\) & \(-24.56\%\) & \(-28.79\%\) & \(-28.95\%\) & \(-19.91\%\) & \(-20.80\%\) & \(-3.36\%\) & \(-4.01\%\) & \(-4.21\%\) \\  & Facade & \(-13.10\%\) & \(-17.09\%\) & \(-18.85\%\) & \(-23.31\%\) & \(-14.72\%\) & \(-16.73\%\) & \(-2.52\%\) & \(-2.98\%\) & \(-3.16\%\) \\ \hline \multirow{10}{*}{Dense} & Longdress\_v & \(-11.64\%\) & \(-15.73\%\) & \(-13.60\%\) & \(-16.62\%\) & \(-14.26\%\) & \(-13.83\%\) & \(-28.97\%\) & \(-28.68\%\) & \(-28.16\%\) \\  & Loot\_v & \(-23.58\%\) & \(-42.83\%\) & \(-38.52\%\) & \(-55.02\%\) & \(-49.73\%\) & \(-52.81\%\) & \(-25.76\%\) & \(-46.07\%\) & \(-54.77\%\) \\  & Redandblack\_v & \(-14.44\%\) & \(-24.04\%\) & \(-19.40\%\) & \(-31.30\%\) & \(-25.57\%\) & \(-21.46\%\) & \(-39.41\%\) & \(-41.84\%\) & \(-43.12\%\) \\  & Soldier\_v & \(-15.28\%\) & \(-39.10\%\) & \(-35.22\%\) & \(-39.88\%\) & \(-44.06\%\) & \(-51.38\%\) & \(-32.42\%\) & \(-34.62\%\) & \(-41.93\%\) \\  & Boxer\_v & \(-13.52\%\) & \(-51.47\%\) & \(-41.99\%\) & \(-41.12\%\) & \(-57.49\%\) & \(-56.59\%\) & \(-34.31\%\) & \(-42.37\%\) & \(-36.54\%\) \\ \hline \multicolumn{2}{c}{**Average Results**} & \(-15.81\%\) & \(-30.28\%\) & \(-29.57\%\) & \(-31.02\%\) & \(-30.71\%\) & \(-32.14\%\) & \(-16.24\%\) & \(-19.59\%\) & \(-20.44\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparisons of the proposed framework with G-PCC PLT [17], G-PCC RAHT [17], and BAAC [18] on point cloud attribute lossy compression. The coding performance improvement is measured by the BD-BR gains. guided graph transform optimization. By refining our texture analysis approach, we aim to mitigate the computational demands and improve the overall efficiency of our scheme.