Modern information technologies and human-centric communication systems employ advanced content representations for richer portrayals of the real world. The newly adopted imaging modalities offer additional information cues and permit the depiction of realistic sceneries, enabling immersive experiences and promoting the engagement of the user with the content. In this context, point clouds have emerged as an attractive option to represent immersive media. This type of visual data has seen a revived interest in the recent years, following the release of low-cost depth sensors and the wide integration of modern graphics processing units in mobile phones and personal computers. Point clouds can be naturally employed in extended reality applications that involve 6 degree-of-freedom interactions, allowing adjustments of the 3-D visual information in a per-point basis. At the same time, complexity reductions are promoted when compared to mesh modelling counterpart, due to the absence of connectivity information and the elimination of corresponding constraints from acquisition to rendering. Yet, the vast amount of information that is required for faithful content representations implies the necessity for efficient data structures and compression algorithms. In particular, new coding schemes must be designed in order to reduce the amount of data and by extension the costs in processing, storage, and transmission of point clouds, while lossy compression solutions should restrain degradations for more appealing results. Furthermore, adequate subjective quality evaluation methodologies tailored to the nature of this 3D imaging modality are essential in order to obtain ground-truth data, and to better understand the impact of compression and rendering artifacts in visual quality. The development of high-performing objective metrics is also fundamental to accurately predict the perceptual quality of degraded models. In this thesis, we address the aforementioned challenges by proposing new subjective quality assessment methodologies that better simulate realistic use-cases of 3D model consumption. We examine several aspects related to point cloud visualization and display means, by exploring different rendering approaches and by introducing experimental set-ups that offer different degrees of interactivity to the end-user. The behavior of human observers in 6 degrees-of-freedom virtual reality scenes is analysed, and visual attention maps are constructed using head and gaze trajectories recorded from eye-tracking experiments. Moreover, navigation data obtained from interactive subjective evaluations in desktop arrangements are exploited to improve image-based quality metrics, whose performance is examined in predicting visual impairments on point cloud contents. In the same line of research, we design novel point-based quality predictors for point cloud topology and texture degradations, and we rigorously analyse their performance using several subjectively annotated data sets. Furthermore, adopting well-established subjective evaluation methodologies, state-of-the-art compression solutions are benchmarked and best practices for rate allocation between geometry and texture encoding are derived. Lastly, a learning-based point cloud compression solution for encoding of both geometric and color information is proposed, and the impact of a series of parameters is examined on the obtained performance to pave the path for future efforts on the field.