RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation

• URL: http://arxiv.org/abs/2503.14154v1
• Date: Tue, 18 Mar 2025 11:25:55 GMT
• Title: RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation
• Authors: Zhang Chen, Shuai Wan, Siyu Ren, Fuzheng Yang, Mengting Yu, Junhui Hou,
• Abstract summary: One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the RB can be optimized for perceptual quality.<n>We propose a novel assessment method, utilizing radial basis function (RBF) to convert discrete point features into a continuous feature function for the distorted point cloud.
• Score: 58.04300937361664
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard practices in PCC highlight a primary issue: while single-feature metrics are widely used to assess compression distortion, the classic method of searching point-to-point nearest neighbors frequently fails to adequately build precise correspondences between point clouds, resulting in an ineffective capture of human perceptual features. To overcome the related limitations, we propose a novel assessment method called RBFIM, utilizing radial basis function (RBF) interpolation to convert discrete point features into a continuous feature function for the distorted point cloud. By substituting the geometry coordinates of the original point cloud into the feature function, we obtain the bijective sets of point features. This enables an establishment of precise corresponding features between distorted and original point clouds and significantly improves the accuracy of quality assessments. Moreover, this method avoids the complexity caused by bidirectional searches. Extensive experiments on multiple subjective quality datasets of compressed point clouds demonstrate that our RBFIM excels in addressing human perception tasks, thereby providing robust support for PCC optimization efforts.

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