Measuring and comparing QoE and simulator sickness of omnidirectional videos in different head mounted displays

Singla, Ashutosh; Fremerey, Stephan; Robitza, Werner; Raake, Alexander

doi:10.1109/qomex.2017.7965658

Cited by 90 publications

(55 citation statements)

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“…For subjective VQA, a handful of testbeds were proposed to subjectively rate the content [40] and streaming [28] of omnidirectional video. Additionally, some subjective experiments were conducted in [20,31,39], finding some key factors that have impact on the visual quality of omnidirectional video under different scenes [20,39] and devices [31]. Additionally, several subjective VQA methods [30,47,51] were proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is necessary to study on visual quality assessment (VQA) for omnidirectional video. Most recently, several subjective VQA approaches [20,28,30,31,39,40,47,51] and objective VQA approaches [35,38,41,45,49,50] have been proposed for either omnidirectional image or video. Among these approaches, the impact of some factors on the quality of omnidirectional video were studied, such as display types, coding schemes and sample uniformity under different map projections.…”

Section: Introductionmentioning

confidence: 99%

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Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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Omnidirectional video enables spherical stimuli with the 360 × 180 • viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video. CCS CONCEPTS• Human-centered computing → Virtual reality; KEYWORDSOmnidirectional video, visual quality assessment, human behavior ACM Reference Format:

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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“…A summary of related work on QoE of 360 video is presented in Table 1. It can be seen that most existing studies focus on the perceptual quality [16,17,[23][24][25][26]. There are only a few studies investigating other QoE aspects such as the presence [14] and cybersickness [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…Previous studies have shown significant impacts of encoding parameters on the perceptual quality, such as quantization parameter (QP) [25], resolution [16,17,[23][24][25], framerate [24], and bitrate [23,24,26]. In comparing the impacts of encoding parameters, a conclusion in [24] is that the resolution and frame rate have very similar impacts on the perceptual quality, and their impacts are more significant than that of the bitrate.…”

Section: Related Workmentioning

confidence: 99%

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A Subjective Study on User Perception Aspects in Virtual Reality

et al. 2019

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Three hundred and sixty degree video is becoming more and more popular on the Internet. By using a Head-Mounted Display, 360-degree video can render a Virtual Reality (VR) environment. However, it is still a big challenge to understand Quality of Experience (QoE) of 360-degree video since user experience during watching 360-degree video is a very complex phenomenon. In this paper, we aim to investigate four QoE aspects of 360-degree video, namely, perceptual quality, presence, cybersickness, and acceptability. In addition, four key QoE-affecting factors of encoding parameters, content motion, rendering device, and rendering mode are considered in our study. To the best of our knowledge, this is the first work that covers a large number of factors and QoE aspects of 360-degree video. In this study, a subjective experiment is conducted using 60 video versions generated from three original 360-degree videos. Based on statistical analysis of the obtained results, various findings on the impacts of the factors on the QoE aspects are provided. In particular, regarding the impacts of encoding parameters, it is found that the difference of QoE is negligible between video versions encoded at 4 K and 2.5 K resolutions. Also, it is suggested that 360-degree video should not be encoded at HD resolution or lower when watching in VR mode using Head Mounted Display. In addition, the bitrate for good QoE varies widely across different video contents. With respect to the content motion factor, its impact is statistically significant on the perceptual quality, presence, and cybersickness. In a comparison of two rendering device sets used in this study, there is no statistically significant difference found for the acceptability and cybersickness. However, the differences of the perceptual quality and presence are indicated to be statistically significant. Regarding the rendering mode, a comparison between VR and non-VR modes is also conducted. Although the non-VR mode always achieves higher perceptual quality scores and higher acceptability rates, more than a half of the viewers prefer the VR mode to the non-VR mode when watching versions encoded at the resolutions of fHD or higher. By contrast, the non-VR mode is preferred at the HD resolution.

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Binaural Evaluation of Sound Quality and Quality of Experience

Raake

Wierstorf

2020

Modern Acoustics and Signal Processing

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Measuring and comparing QoE and simulator sickness of omnidirectional videos in different head mounted displays

Cited by 90 publications

References 9 publications

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

A Subjective Study on User Perception Aspects in Virtual Reality

Binaural Evaluation of Sound Quality and Quality of Experience

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