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.

本研究介绍了一种基于谱图小波的全参考点云质量评估方法，利用新颖的基于谱图小波的度量标准对参考和失真点云之间的坐标和颜色信号进行比较，并通过支持向量回归将多个传统全参考度量标准和基于谱图小波的度量标准整合，从而实现准确的点云质量评估。实验结果表明，所提出的点云质量评估标准与主观质量评分更准确地相关联，相比传统点云质量评估标准效果更好。

基于谱图小波的全参考点云质量评估

Full-reference Point Cloud Quality Assessment Using Spectral Graph  Wavelets

Objective image quality assessment (IQA) is imperative in the current
multimedia-intensive world, in order to assess the visual quality of an image
at close to a human level of ability. Many~parameters such as color intensity,
structure, sharpness, contrast, presence of an object, etc., draw human
attention to an image. Psychological vision research suggests that human vision
is biased to the center area of an image and display screen. As a result, if
the center part contains any visually salient information, it draws human
attention even more and any distortion in that part will be better perceived
than other parts. To the best of our knowledge, previous IQA methods have not
considered this fact. In this paper, we propose a full reference image quality
assessment (FR-IQA) approach using visual saliency and contrast; however, we
give extra attention to the center by increasing the sensitivity of the
similarity maps in that region. We evaluated our method on three large-scale
popular benchmark databases used by most of the current IQA researchers
(TID2008, CSIQ~and LIVE), having a total of 3345 distorted images with
28~different kinds of distortions. Our~method is compared with 13
state-of-the-art approaches. This comparison reveals the stronger correlation
of our method with human-evaluated values. The prediction-of-quality score is
consistent for distortion specific as well as distortion independent cases.
Moreover, faster processing makes it applicable to any real-time application.
The MATLAB code is publicly available to test the algorithm and can be found
online at this http URL

本文提出了一种基于视觉显著性和对比度的全参考图像质量评估方法，该方法增加了中心区域的敏感度，通过在三个大规模的基准数据库上的实验验证，显示出与人类评估数值更强的相关性和更高的实时处理速度，并提供 MATLAB 代码以供测试。