论文标题
基于人类视觉感知机制的渐进知识转移，用于对点云的感知质量评估
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds
论文作者
论文摘要
由于彩色点云在许多领域的广泛应用，由于存在各个阶段引入的质量降解，因此点云知觉质量评估在视觉通信系统中起着至关重要的作用。但是，现有的点云质量评估忽略了人类视觉系统（HVS）的机制，这对感知质量评估的准确性有重要影响。在本文中，提出了基于人类视觉感知机制的渐进知识转移，用于对点云的感知质量评估（PKT-PCQA）。 PKT-PCQA合并了来自相邻区域的本地特征，并从图形频谱中提取的全局特征。考虑到HVS属性，在PKT-PCQA中也考虑了空间和通道注意机制。此外，PKT-PCQA受到人类大脑的分层感知系统的启发，采用了渐进知识转移，以将粗粒质量的质量分类知识转换为精细的质量预测任务。在三个大型和独立的点云评估数据集上的实验表明，与最先进的完整参考质量评估方法相比，提出的无参考PKT-PCQA网络可以更好地获得等效性能，因此，不超过存在的参考质量评估网络。
With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT-PCQA merges local features from neighboring regions and global features extracted from graph spectrum. Taking into account the HVS properties, the spatial and channel attention mechanism is also considered in PKT-PCQA. Besides, inspired by the hierarchical perception system of human brains, PKT-PCQA adopts a progressive knowledge transfer to convert the coarse-grained quality classification knowledge to the fine-grained quality prediction task. Experiments on three large and independent point cloud assessment datasets show that the proposed no reference PKT-PCQA network achieves better of equivalent performance comparing with the state-of-the-art full reference quality assessment methods, outperforming the existed no reference quality assessment network.
