Su Honglei, Liu Qi, Liu Yuxin, Yuan Hui, Yang Huan, Pan Zhenkuan, Wang Zhou
IEEE Trans Image Process. 2023;32:1815-1828. doi: 10.1109/TIP.2023.3253252. Epub 2023 Mar 17.
With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost.
随着在带宽受限情况下对3D点云压缩和流式传输的需求不断增加，准确高效地确定压缩点云的质量变得越来越重要，以便评估和优化终端用户的体验质量（QoE）。在此，我们首次尝试之一是开发一种基于比特流的无参考（NR）模型，用于点云的感知质量评估，而无需对压缩数据流进行完全解码。具体而言，我们首先基于经验率失真模型建立纹理复杂度与比特率和纹理量化参数之间的关系。然后，我们根据纹理复杂度和量化参数构建纹理失真评估模型。通过将此纹理失真模型与从Trisoup几何编码参数导出的几何失真模型相结合，我们获得了一个名为streamPCQ的基于比特流的整体NR点云质量模型。实验结果表明，与现有的经典全参考（FR）和简化参考（RR）点云质量评估方法相比，所提出的streamPCQ模型在计算成本仅为其一小部分的情况下，展现出极具竞争力的性能。
