# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 4 Experimental Results
### Ablation Studies  
The primary contribution of this paper lies in the introduction of a novel texture-guided graph transform optimization scheme. In our approach, texture priors derived from the proposed intra-cluster attribute analysis serve as the desired graph sparsity during the graph construction process for point cloud clusters. Subsequently, this desired graph sparsity is utilized in both the graph connectivity mask **A** (as per Equation (14)) and the  
Figure 5: Subjective quality comparison of decoded point clouds between our scheme and the state of the art on the _Longdress_vox10_1300_ and _Thaidancer_viewedep_vox12_. (**a**) Subjective quality evaluation on _Longdress_vox10_1300_. (**b**) Subjective quality evaluation on _Thaidancer_viewedep_vox12_. setting of the optimization parameter \(\theta=\frac{1}{\sqrt{\alpha\beta}}\) (as per Equation (16)). To objectively assess the effectiveness of our texture-guided graph connectivity mask **A** and the sparsity-related parameter \(\theta\) on point cloud attribute coding performance, we conduct ablation studies to independently analyze each module as follows. **Validation of the Texture-guided Graph Connectivity Mask:** As detailed in Section 3.4, we propose an intra-cluster attribute analysis scheme to extract texture priors. These texture priors are then leveraged to construct a texture-guided graph connectivity mask for graph optimization. To validate the impact of the proposed texture-guided graph connectivity mask, we conduct a series of comparison experiments between our scheme with the texture-guided graph connectivity mask and the scheme employing fixed graph sparsity values \(k\). Specifically, we conduct three tests with \(k\) set to 5, 10, and 15 for this validation. Results in Table 4 demonstrate significant coding performance losses introduced with fixed graph sparsity values, even across different settings. For example, the experiment with \(k\) set to 5 presents the average BD-BR loss of 6.50%, 8.25%, and 8.20% on Y, U, and V components, respectively. This validation underscores the performance improvement of the proposed texture-guided scheme for point cloud attribute compression. By leveraging the texture-guided graph connectivity mask, we achieve a more precise representation of attributes within the underlying graph, surpassing conventional graph construction methods reliant solely on fixed graph sparsity values. **Validation of the Sparsity-related Optimization Parameter \(\theta\):** As illustrated in Section 3.5, we adjust the optimization parameter \(\theta\) in Equation (16) relative to the desired sparsity obtained from our attribute analysis process. The optimization parameter \(\theta\) then is used to adjust the weight of the graph sparsity term \(R_{s}(\textbf{W})\) within the rate-distortion-sparsity energy function in Equation (9). The graph sparsity term plays a pivotal role in the graph optimization process, influencing the decorrelation capability of the constructed graph transform. To validate the impact of our sparsity-related optimization parameter, we conduct comparison experiments between our scheme with the sparsity-related optimization parameter and the scheme employing fixed optimization parameter values \(\theta\). Specifically, three tests with \(\theta\) set to 0.01, 0.1, and 1 are conducted for this validation. The results presented in Table 5 demonstrate the effectiveness of our sparsity-related