International audienceIn this chapter different factors that may influence Quality of Experience (QoE) in the context of media consumption, networked services, and other electronic communication services and applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling into three categories: human, system and context influence factors (IFs). With respect to Human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it

This paper presents a quality evaluation of the point cloud codecs recently standardised by the MPEG committee. A subjective experiment was designed to evaluate these codecs performance in terms of bit rate versus perceived quality. Four laboratories with experience with such studies carried out the subjective evaluation. Although the exact setups of the different laboratories were not the same, the obtained MOS results exhibit a high correlation between them, confirming reliability and repeatability of the proposed assessment protocol. The study also confirmed MPEG V-PCC as a superior compression solution for static point clouds when compared to MPEG G-PCC. Finally, a benchmark of the most popular point cloud metrics was performed based on the subjective results. The point2plane metric using the mean square error as a distance measure was revealed to have the best correlation with subjective scores, closely followed by the point2point, also using the mean square error. As both metrics produce high correlation results, it can be concluded that they can be used for quality assessment of MPEG codecs.

Abstract-Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octreebased compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective quality assessment tools.

Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between metrics. Results demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable predictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with important applications in the entertainment, automotive and geographical mapping industries. There is some consensus that state-of-the-art solutions for efficient storage and communication of point clouds are far from satisfactory. This paper describes a study on point cloud quality evaluation, conducted in the context of JPEG Pleno to help define the test conditions of future compression proposals. A heterogeneous set of static point clouds in terms of number of points, geometric structure and represented scenarios were selected and compressed using octree-pruning and a projection-based method, with three different levels of degradation. The models were comprised of both geometrical and color information and were displayed using point sizes large enough to ensure observation of watertight surfaces. The stimuli under assessment were presented to the observers on 2D displays as animations, after defining suitable camera paths to enable visualization of the models in their entirety and realistic consumption. The experiments were carried out in three different laboratories and the subjective scores were used in a series of correlation studies to benchmark objective quality metrics and assess inter-laboratory consistency.