Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913391172386816 |
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| author | Watanabe, Ryosuke Nonaka, Keisuke Pavez, Eduardo Kobayashi, Tatsuya Ortega, Antonio |
| author_facet | Watanabe, Ryosuke Nonaka, Keisuke Pavez, Eduardo Kobayashi, Tatsuya Ortega, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09762 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets Watanabe, Ryosuke Nonaka, Keisuke Pavez, Eduardo Kobayashi, Tatsuya Ortega, Antonio Computer Vision and Pattern Recognition Multimedia Signal Processing 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. |
| title | Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets |
| topic | Computer Vision and Pattern Recognition Multimedia Signal Processing |
| url | https://arxiv.org/abs/2406.09762 |