CVMMIVMar 18, 2025

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

arXiv:2503.14154v12 citationsh-index: 17IEEE transactions on multimedia
Originality Incremental advance
AI Analysis

This addresses the challenge of perceptual quality assessment for compressed point clouds, which is crucial for optimizing codecs, though it appears incremental as it builds on existing metrics by improving correspondence accuracy.

The paper tackles the problem of evaluating perceived distortion in point cloud compression by proposing RBFIM, a method that uses radial basis function interpolation to establish precise correspondences between point clouds, which significantly improves assessment accuracy as demonstrated in experiments on multiple subjective quality datasets.

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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