Improving Point Cloud Quality Metrics with Noticeable Possibility Maps

Point cloud quality assessment (PCQA) plays a vital role in the quality of experience (QoE) oriented data processing. To reflect the visual degradation introduced by various distortions, many PCQA metrics have been proposed in recent years. However, these metrics often take all distortions indiscriminately into account, ignoring the fact that some distortions are below the noticeable threshold and thus do not affect subjective perception. To solve this problem, we involve the characteristic of just noticeable difference (JND) into PCQA. Specifically, we first rotate the reference and distorted point cloud repeatedly to obtain multiple perspectives, then utilize the 2D JND models to derive the 3D noticeable possibility maps (NPM) to infer the possibility that the distortion is perceivable at each point. Utilizing the generated NPM, we modify current point-wise and structure-wise quality metrics to help them correlate better with subjective perception. Extensive experiments show the universal effectiveness of the proposed NPM in improving PCQA metrics. Code will be available at https://github.com/NekoooooOoi/NPM.