首个针对三明治提升编码点云的无参考质量评估模型，无需完整解码即可实时监测。
Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds
- 基于比特流分析，结合纹理码率、复杂度与量化参数建模纹理失真
- 在3个数据集上性能超越现有方法，计算开销更低
- 适用于点云传输网络的质量监控，适合工程部署
无参考比特流层点云质量评估（PCQA）可在任意网络节点无需完整解码的情况下实现实时质量监控。本文首次为三明治提升（Trisoup-Lifting）编码点云构建了专用的无参考比特流层PCQA模型。在几何编码无损的前提下，研究了每点纹理码率（TBPP）、纹理复杂度（TC）与纹理量化参数（TQP）之间的关系，利用TQP和TBPP估计TC；进而构建基于TC、TBPP和TQP的纹理失真评估模型。最终，通过融合该纹理失真模型与由三明治节点尺寸对数（tNSL）决定的几何衰减因子，得到综合性的无参考比特流层PCQA模型streamPCQ-TL。此外，本文构建了首个且最大的专用于三明治提升编码模式的PCQA数据库WPC6.0，包含400个失真点云，涵盖4种几何×5种纹理失真级别。在M-PCCD、ICIP2020及自建的WPC6.0数据库上的实验结果表明，所提streamPCQ-TL模型在鲁棒性与性能上均优于现有先进指标，尤其在计算成本方面表现突出。数据集与源码将公开发布于https://github.com/qdushl/Waterloo-Point-Cloud-Database-6.0。
原文摘要 · Abstract (English)
No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we develop the first PCQA model dedicated to Trisoup-Lifting encoded 3D point clouds by analyzing bitstreams without full decoding. Specifically, we investigate the relationship among texture bitrate per point (TBPP), texture complexity (TC) and texture quantization parameter (TQP) while geometry encoding is lossless. Subsequently, we estimate TC by utilizing TQP and TBPP. Then, we establish a texture distortion evaluation model based on TC, TBPP and TQP. Ultimately, by integrating this texture distortion model with a geometry attenuation factor, a function of trisoupNodeSizeLog2 (tNSL), we acquire a comprehensive NR bitstream-layer PCQA model named streamPCQ-TL. In addition, this work establishes a database named WPC6.0, the first and largest PCQA database dedicated to Trisoup-Lifting encoding mode, encompassing 400 distorted point clouds with both 4 geometric multiplied by 5 texture distortion levels. Experiment results on M-PCCD, ICIP2020 and the proposed WPC6.0 database suggest that the proposed streamPCQ-TL model exhibits robust and notable performance in contrast to existing advanced PCQA metrics, particularly in terms of computational cost. The dataset and source code will be publicly released at https://github.com/qdushl/Waterloo-Point-Cloud-Database-6.0
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