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Multimedia systems are typically optimized in a way that maximizes users’ satisfaction of using the systems/services. This user satisfaction is what is commonly referred to as Quality of Experience (QoE). For visual media, such as images and videos, the optimization of QoE has meant reducing the visibility of artifacts (e.g. noise or other disturbing factors) in the visual media. This is based on the assumption that the sole appearance of artifacts would disrupt the whole visual experience, in a world where media weremostly consumed passively, and in well defined contexts (e.g., TV broadcasts). Nowadays, the way users experience visual media has changed, thanks to the diffusion of mobile, interactive, immersive, and on-demand technology. Media are now consumed in many different contexts, for example, in the interactive and customizable contexts of social media, or in the immersive contexts of virtual and augmented reality. As consequence of these developments, a user’s visual QoE is no longer determined solely on the appearance of artifacts, but also by factors relevant to the viewing context. This thesis brings in new insights in modeling and automatically assessing users’ visual QoE in view of the developments above. The thesis starts with looking into subjective methodologies for QoE assessments, and continues with developing objective quality metrics that incorporate QoE influencing factors to improve state-of-the-art metrics. Developing reliable and accurate objective metrics to automatically assess users’ visual QoE requires subjective data that are reliable as well. This thesis argues that existing methodologies for collecting subjective data might not be reliable when used to evaluate QoE factors that are highly subjective, or that are new to the research community. Highly subjective quantities may yield different conclusions across experiments. As for new types of media, they often bring the uncertainty on how to evaluate them. Two studies are then presented in this regard. The first study considers the assessment of image aesthetic appeal, as one example of a highly subjective quantity. A large scale study was conducted to compare the use of different subjective methodologies to collect aesthetic appeal data, and some ways to measure the data reliably were proposed. The second study considers the assessment of point cloud quality, as one example of a new type of media (i.e. immersive media). The study explores quantitative and qualitative approaches to understand the way users judge point cloud images. Following the studies on subjective QoE assessments, two studies on objective QoE metrics are presented in this thesis. Despite existing efforts to model the influence of different factors on visual QoE, limited work have proposed to incorporate these factors into existing objective qualitymetrics to improve state-of-the-art. The first study on objective QoE metrics in this thesis investigates the influence of image content/semantic categories (i.e. scene and object categories) on visual QoE, and proposes to include semantic category features in objective image quality metrics. The proposed approach shows improvement from state-of-the-art in predicting image quality. The next study on objective quality metrics investigates new QoE influencing factors for point cloud images, and proposes to incorporate these into an objective quality metric for point cloud images. The results of the studies presented in this thesis show how existing subjective methodologies could yield reliable aesthetic appeal data, and explore point cloud QoE influencing factors. Moreover, the results show that incorporating new QoE influencing factors into objective image quality metrics could improve state-of-the-art performance in predicting users’ QoE. At the end of this thesis, some recommendations are given for future research following up the findings in this thesis.
Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date16 Oct 2018
Print ISBNs978-94-028-1188-9
DOIs
Publication statusPublished - 2018

    Research areas

  • Quality of Experience (QoE), image quality metrics, subjective methodologies

ID: 46830147