PointPCA+: A full-reference Point Cloud Quality Assessment metric with PCA-based features

Abstract

This paper introduces an enhanced Point Cloud Quality Assessment (PCQA) metric, termed PointPCA+, as an extension of PointPCA, with a focus on computational simplicity and feature richness. PointPCA+ refines the original PCA-based descriptors by employing Principal Component Analysis (PCA) solely on geometry data; additionally, the texture descriptors are refined through a direct application of the function on YCbCr values, enhancing the efficiency of computation. The metric combines geometry and texture features, capturing local shape and appearance properties, through a learning-based fusion to generate a total quality score. Prior to fusion, a feature selection module is incorporated to identify the most effective features from a proposed super-set. Experimental results demonstrate the high predictive performance of PointPCA+ against subjective ground truth scores obtained from four publicly available datasets. The metric consistently outperforms state-of-the-art solutions, offering valuable insights into the design of similarity measurements and the effectiveness of handcrafted features across various distortion types. The code of the proposed metric is available at https://github.com/cwi-dis/pointpca_suite/.