Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets

• URL: http://arxiv.org/abs/2406.09762v1
• Date: Fri, 14 Jun 2024 06:59:54 GMT
• Title: Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets
• Authors: Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez, Tatsuya Kobayashi, Antonio Ortega,
• Abstract summary: Point clouds in 3D applications frequently experience quality degradation during processing, e.g., scanning and compression. This paper introduces a full-reference (FR) PCQA method utilizing spectral graph wavelets (SGWs) To our knowledge, this is the first study to introduce SGWs for PCQA.
• Score: 29.126056066012264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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