Full reference point cloud quality assessment using support vector regression
<|reference_start|>Full reference point cloud quality assessment using support vector regression: Point clouds are a general format for representing realistic 3D objects in diverse 3D applications. Since point clouds have large data sizes, developing efficient point cloud compression methods is crucial. However, excessive compression leads to various distortions, which deteriorates the point cloud quality perceived by end users. Thus, establishing reliable point cloud quality assessment (PCQA) methods is essential as a benchmark to develop efficient compression methods. This paper presents an accurate full-reference point cloud quality assessment (FR-PCQA) method called full-reference quality assessment using support vector regression (FRSVR) for various types of degradations such as compression distortion, Gaussian noise, and down-sampling. The proposed method demonstrates accurate PCQA by integrating five FR-based metrics covering various types of errors (e.g., considering geometric distortion, color distortion, and point count) using support vector regression (SVR). Moreover, the proposed method achieves a superior trade-off between accuracy and calculation speed because it includes only the calculation of these five simple metrics and SVR, which can perform fast prediction. Experimental results with three types of open datasets show that the proposed method is more accurate than conventional FR-PCQA methods. In addition, the proposed method is faster than state-of-the-art methods that utilize complicated features such as curvature and multi-scale features. Thus, the proposed method provides excellent performance in terms of the accuracy of PCQA and processing speed. Our method is available from https://github.com/STAC-USC/FRSVR-PCQA.<|reference_end|>
@article{watanabe2024full,
    title={Full reference point cloud quality assessment using support vector
  regression},
    author={Ryosuke Watanabe, Shashank N. Sridhara, Haoran Hong, Eduardo Pavez,
  Keisuke Nonaka, Tatsuya Kobayashi, Antonio Ortega},
    journal={arXiv preprint arXiv:2406.10520},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10520},
    primaryClass={cs.CV eess.IV eess.SP}
}