Compressed Point Cloud Quality Index by Combining Global Appearance and Local Details

Abstract

In recent years, many standardized algorithms for point cloud compression (PCC) has been developed and achieved remarkable compression ratios. To provide guidance for rate-distortion optimization and codec evaluation, point cloud quality assessment (PCQA) has become a critical problem for PCC. Therefore, in order to achieve a more consistent correlation with human visual perception of a compressed point cloud, we propose a full-reference PCQA algorithm tailored for static point clouds in this article, which can jointly measure geometry and attribute deformations. Specifically, we assume that the quality decision of compressed point clouds is determined by both global appearance (e.g., density, contrast, and complexity) and local details (e.g., gradient and hole). Motivated by the nature of compression distortions and the properties of the human visual system, we derive perceptually effective features for the above two categories, such as content complexity, luminance/geometry gradient, and hole probability. Through systematically incorporating measurements of variations in the local and global characteristics, we derive an effective quality index for the input compressed point clouds. Extensive experiments and analyses conducted on popular PCQA databases show the superiority of the proposed method in evaluating compression distortions. Subsequent investigations validate the efficacy of different components within the model design.