Understanding perceptual mesh quality in Virtual Reality and desktop settings

Alfarasani, Dalia 2024. Understanding perceptual mesh quality in Virtual Reality and desktop settings. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

This thesis focuses on 3D mesh quality, essential for immersive VR applications. It examines subjective methodologies for Quality of Experience (QoE) assessments and then develops objective quality metrics incorporating QoE influencing factors. Existing studies consider 3D mesh quality on the desktop. The perceptual quality in a Virtual Reality(VR) setting can be different, this inspired us to measure mesh quality in a VR setting, which has been the subject of limited studies in this area. We consider how different 3D distortion types affect perceptual quality of 3D when viewed in a VR setup. In our experiment findings, in the VR setting, perception appears more sensitive to particular distortions than others, compared with the desktop. This can provide helpful guidance for downstream applications. Furthermore, we evaluate state-of-the-art perceptually inspired mesh difference metrics for predicting objective quality scores captured in VR and compare them with the desktop. The experimental results show that subjective scores in the VR setting are more consistent than those on desktop setting. As we focus on a better understanding of perceptual mesh quality, we further consider the problem of mesh saliency, which measures the perceptual importance of different regions on a mesh. However, existing mesh saliency models are largely built with hard coded formulae or utilise indirect measures, which cannot capture true human perception. In this thesis, to generate ground truth mesh saliency, we use subjective studies that collect eye-tracking data from participants and develop a method for mapping the eye-tracking data of individual views consistently onto a mesh. We further evaluate existing methods of measuring saliency and propose a new machine learning-based method that better predicts subjective saliency values. The predicted saliency is also demonstrated to help with mesh quality prediction as salient regions tend to be more important perceptually, leading to a novel effective mesh quality measure.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Date of First Compliant Deposit:	30 May 2024
Date of Acceptance:	30 May 2024
Last Modified:	31 May 2024 07:02
URI:	https://orca.cardiff.ac.uk/id/eprint/169288

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