The Impact of Black Edge Artifact on QoE of the FOV-Based Cloud VR Services

Song, Jiarun; Mao, Xionghui; Yang, Fuzheng

doi:10.1109/tmm.2022.3232229

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…23. These results are obtained by mapping our black border data to the MOS values (ranging from one to five) from the previously cited QoE study [11]. This figure indicates that the MOS in the NOP case sharply declines as the anticipation time increases due to the expanded presence of black borders within the user's visual range.…”

Section: Impact Of Prediction On Vr Qualitymentioning

confidence: 59%

“…However, with increased end-to-end latency, larger black borders may appear post-reprojection, compromising user immersion [10], [45]. QoE research [11] has demonstrated that black borders significantly affect the user's gameplay experience, reducing the mean opinion score (MOS) as the presence of black borders increases. Consequently, smaller We show the impacts of motion prediction on the formation of black borders in Figs.…”

Section: Impact Of Prediction On Vr Qualitymentioning

confidence: 99%

“…The image reprojection technique [7]- [9]-in which a rendered frame is adjusted to account for head pose changes that occur after the scene is renderedhelps to reduce mismatch and alleviate motion sickness. However, as the end-to-end latency increases, large black borders may begin to appear after reprojection [5], [10], negatively impacting the user's quality of experience (QoE) [11].…”

Section: Introductionmentioning

confidence: 99%

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Velocity- and Error-Aware Switching of Motion Prediction Models for Cloud Virtual Reality

Hermawan,

Luckyarno,

Fauzi

et al. 2023

IEEE Access

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Offloading virtual reality (VR) computations to a cloud computing entity can enable support for VR services on low-end user devices but may result in increased latency, which will lead to mismatch between the user's viewport and the received VR image, thus inducing motion sickness. Predicting future motion and rendering future images accordingly is a promising solution to the latency problem. In this paper, we develop velocity-and error-aware model switching schemes applicable to a wide range of existing motion prediction models. First, we consider the chattering problem of machine learning (ML)based prediction models and the relationship between the velocity and the prediction error gap between an ML model and the case of no prediction (NOP). Accordingly, we propose a velocity-aware switching (VAS) scheme that combines the outputs from the ML model and the NOP case via a weight determined by the head motion velocity. Next, we develop an ensemble method combining a set of outputs from VAS and other models, called error-aware switching (EAS). EAS switches between model outputs based on the error statistics of those outputs under the parallel execution of multiple models, including VAS models. For EAS, schemes for both hard switching and soft integration of the model outputs are proposed. We evaluate the proposed schemes based on real VR motion traces for diverse ML-based prediction models.

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Section: Impact Of Prediction On Vr Qualitymentioning

confidence: 59%

Section: Impact Of Prediction On Vr Qualitymentioning

confidence: 99%