VQA-CPC: a novel visual quality assessment metric of color point clouds

Color point clouds can provide users more realistic visual information and better immersive experience than traditional imaging techniques. How to evaluate the visual quality of color point clouds accurately is an important issue to be solved urgently. In this work, we propose a novel full reference metric, called as Visual Quality Assessment of Color Point Clouds (VQA-CPC). Starting from the geometry and texture of color point cloud, the proposed metric calculates the distances from color point cloud’s points to their geometric centroid and the distances from the texture coordinates of the points to texture centroid. Then, a measuring distortion strategy based on distortion measurement is designed and used to extract the features of color point cloud. Finally, the extracted geometric features and texture features are used to construct the feature vector and predict quality of the distorted color point cloud. Moreover, we construct a color point cloud database, called as NBU-PCD1.0, for verifying the effectiveness of the proposed metric. Experimental results show that the proposed VQA-CPC metric is better than the existing point cloud metrics.