Lv Jianyu, Su Honglei, Liu Qi, Yuan Hui
IEEE Trans Vis Comput Graph. 2024 Aug 15;PP. doi: 10.1109/TVCG.2024.3443911.
No-reference point cloud quality assessment (PCQA) based on bitstreams uses information extracted from the bitstream for quality monitoring at network nodes. We develop a no-reference PCQA model based on bitstreams for the perceived quality assessment of Octree-Lifting coded point clouds. At first, our research explores the essential correlation between subjective visual quality degradation and the texture quantization parameter (TQP) when using lossless geometric coding. Then, we enhance the proposed model by incorporating texture complexity (TC) while taking into account the dependence of perceptual coding distortion on the texture characteristics of a point cloud. We estimate TC by utilizing TQP and calculating the average standard deviation of the Y-component of the attribute value ( Y_ std), both of which are extracted from the bitstream. Then, a texture distortion assessment model is constructed based on TQP and Y_ std. The integration of the texture distortion model with the position quantization scale (PQS) results in the derivation of an overall no-reference bitstream-based PCQA model, named streamPCQ-OL. The findings from the conducted experiments highlight a significant superiority of the proposed model over existing approaches in terms of performance. The dataset and source code will be publicly released and made available for access at https://github.com/qdushl/Waterloo-Point-Cloud-Database-4.0.
基于比特流的无参考点云质量评估（PCQA）利用从比特流中提取的信息在网络节点进行质量监测。我们开发了一种基于比特流的无参考PCQA模型，用于八叉树提升编码点云的感知质量评估。首先，我们的研究探索了在使用无损几何编码时主观视觉质量退化与纹理量化参数（TQP）之间的本质关联。然后，我们通过纳入纹理复杂度（TC）来增强所提出的模型，同时考虑感知编码失真对点云纹理特征的依赖性。我们通过利用TQP并计算从比特流中提取的属性值的Y分量的平均标准差（Y_std）来估计TC。然后，基于TQP和Y_std构建纹理失真评估模型。将纹理失真模型与位置量化尺度（PQS）相结合，得出一个基于比特流的整体无参考PCQA模型，名为streamPCQ-OL。所进行实验的结果突出了所提出模型在性能方面相对于现有方法的显著优势。数据集和源代码将在https://github.com/qdushl/Waterloo-Point-Cloud-Database-4.0上公开发布并可供访问。
