I. Introduction
Nowadays, several 3D representation formats have become very popular, such as light-fields and point clouds. These formats enable applications in many fields and thus, some multimedia applications have now become a reality, e.g. real-time 3D immersive telepresence, 3D free viewpoint broadcasting and geographical information systems among others [1]. Recently, there is an increasing interest in the point-based representation, which models real world objects with a cloud of sampled points on its surface [2]. The point cloud is a 3D representation of the real visual world by a set of 3D coordinates (points) with some additional attributes such as color [3]. With the advent of 3D acquisition systems, it is now possible to capture a point cloud representation of a visual scene with a very high resolution [4]. For example, Light Detection and Ranging (LIDAR) scanners can sample a large amount of points at very high frame rates. This 3D location data can be up to billions of points and storing and transmitting this information will require a significant amount of memory and transmission bandwidth. Therefore, storage and transmission of large point clouds requires the development of efficient point cloud compression solutions [5]–[7]. In this context, to have a wide adoption of this representation model, it is necessary to measure the quality of experience for the end users. Unfortunately, it is not yet clear how reliable are the most used quality metrics to express the user experience quality.