In this paper, I present the proposal of a virtual reality subjective experiment to be performed at Texas State University, which is part of the VQEG-IMG test plan for the definition of a new recommendation for subjective assessment of eXtended Reality (XR) communications (work item ITU-T P.IXC). More specifically, I discuss the challenges of estimating the user quality of experience (QoE) for immersive applications and detail the VQEG-IMG test plan tasks for XR subjective QoE assessment. I also describe the experimental choices of the audio-visual experiment to be performed at Texas State University, which has the goal of comparing two possible scenarios for teleconference meetings: a virtual reality representation and a realistic representation.
Augmented and Virtual Reality (VR) technology has recently proved to be useful in many learning and training scenarios. VR applications designed to practice communication skills (also known as Public Speaking Training or PST) are currently among the newest and still-unexplored solutions whose effectiveness is still to be tested. The current paper evaluates the Quality of Experience for a public speaking VR system where speakers can experience a talk session in front of a wide audience that actively reacts to their statements. A double stimulus experiment (with a real and virtual audience) was carried out in order to measure the visual quality, the immersiveness and the effectiveness of the approach. Objective evaluations, users’ feedback and public speaking metrics showed that the VR set-up enhanced speakers’ gesture and speech control when compared to performing in front of a real audience.
This paper presents a study on Quality of Experience (QoE) evaluation of 3D objects in Mixed Reality (MR) scenarios. In particular, a subjective test was performed with Microsoft HoloLens, considering different degradations affecting the geometry and texture of the content. Apart from the analysis of the perceptual effects of these artifacts, given the need for recommendations for subjective assessment of immersive media, this study was also aimed at: 1) checking the appropriateness of a single stimulus methodology (ACR-HR) for these scenarios where observers have less references than with traditional media, and 2) analyzing the possible impact of environment lighting conditions on the quality evaluation of 3D objects in mixed reality (MR), and 3) benchmark state-of-the-art objective metrics in this context. The subjective results provide insights for recommendations for subjective testing in MR/AR, showing that ACR-HR can be used in similar QoE tests and reflecting the influence among the lighting conditions, the content characteristics, and the type of degradations. The objective results show an acceptable performance of perceptual metrics for geometry quantization artifacts and point out the need of further research on metrics covering both geometry and texture compression degradations.
In the last decades, many researchers have developed algorithms that estimate the quality of a visual content (videos or images). Among them, one recent trend is the use of texture descriptors. In this paper, we investigate the suitability of using Binarized Statistical Image Features (BSIF), the Local Configuration Pattern (LCP), the Complete Local Binary Pattern (CLBP), and the Local Phase Quantization (LPQ) descriptors to design a referenceless image quality assessment (RIQA) method. These descriptors have been successfully used in computer vision applications, but their use in image quality assessment has not yet been thoroughly investigated. With this goal, we use a framework that extracts the statistics of these descriptors and maps them into quality scores using a regression approach. Results show that many of the descriptors achieve a good accuracy performance, outperforming other state-of-the-art RIQA methods. The framework is simple and reliable.