A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.

Virtual reality (VR) environment can provide immersive experience to viewers. Under the VR environment, providing a good quality of experience is extremely important. Therefore, in this paper, we present an image quality assessment (IQA) study on omnidirectional images. We first build an omnidirectional IQA (OIQA) database, including 16 source images with their corresponding 320 distorted images. We add four commonly encountered distortions. These distortions are JPEG compression, JPEG2000 compression, Gaussian blur, and Gaussian noise. Then we conduct a subjective quality evaluation study in the VR environment based on the OIQA database. Considering that visual attention is more important in VR environment, head and eye movement data are also tracked and collected during the quality rating experiments. The 16 raw and their corresponding distorted images, subjective quality assessment scores, and the head-orientation data and eye-gaze data together constitute the OIQA database. Based on the OIQA database, we test some state-of-the-art full-reference IQA (FR-IQA) measures on equirectangular format or cubic format omnidirectional images. The results show that applying FR-IQA metrics on cubic format omnidirectional images could improve their performance. The performance of some FR-IQA metrics combining the saliency weight of three different types are also tested based on our database. Some new phenomena different from traditional IQA are observed.