  Point clouds in 3D applications frequently experience quality degradation
during processing, e.g., scanning and compression. Reliable point cloud quality
assessment (PCQA) is important for developing compression algorithms with good
bitrate-quality trade-offs and techniques for quality improvement (e.g.,
denoising). This paper introduces a full-reference (FR) PCQA method utilizing
spectral graph wavelets (SGWs). First, we propose novel SGW-based PCQA metrics
that compare SGW coefficients of coordinate and color signals between reference
and distorted point clouds. Second, we achieve accurate PCQA by integrating
several conventional FR metrics and our SGW-based metrics using support vector
regression. To our knowledge, this is the first study to introduce SGWs for
PCQA. Experimental results demonstrate the proposed PCQA metric is more
accurately correlated with subjective quality scores compared to conventional
PCQA metrics.
