| 1. |
- Chu, Thi My Chinh, et al.
(author)
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On the Perception of Frame Stalls in Remote VR for Task and Task-Free Subjective Tests
- 2023
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In: 2023 15th International Conference on Quality of Multimedia Experience, QoMEX 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350311730 ; , s. 201-204
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Conference paper (peer-reviewed)abstract
- The performance of remotely rendered Virtual Reality (VR) is sensitive to temporal disturbances in communication channels. An earlier Quality of Experience (QoE) study of temporal impacts in the form of frame stalls has revealed difficulties with subjective disturbance ratings while performing a task in an interactive 6-degrees-of-freedom (DOF) VR environment. This study follows up on above observation by comparing QoE ratings in the presence and absence of a task. The exploratory findings show that the task-free subjective tests yield lower ratings compared to the subjective tests with task. This indicates that the participants became more sensitive to temporal impairments in the absence of a task. Also, the positive impact of reprojection on the QoE ratings decreased in the task-free environment. The simulator sickness results for individual symptoms were on similar low levels in both settings. The total score (TS) of sickness severity was higher after than before the subjective tests with task while the difference between the TS before and after the task-free subjective tests was insignificant, © 2023 IEEE.
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| 2. |
- Fiedler, Markus
(author)
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Performance analytics of a virtual reality streaming model
- 2020
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer. - 9783030430238 ; , s. 1-18
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Conference paper (peer-reviewed)abstract
- This work focuses on post-analysis of performance results by means of Performance Analytics. The results to be post-analysed are provided by a Stochastic Fluid Flow Model (SFFM) of Virtual Reality (VR) streaming. Performance Analytics implies using the Machine Learning (ML) algorithm M5P for constructing model trees, which we examine amongst others for asymptotic behaviours and parameter impacts in both uni- and multivariate settings. We gain valuable insights into key parameters and related thresholds of importance for good VR streaming performance. © Springer Nature Switzerland AG 2020.
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| 3. |
- Hosfeld, Tobias, et al.
(author)
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From QoS distributions to QoE distributions : A system's perspective
- 2020
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In: Proceedings of the 2020 IEEE Conference on Network Softwarization. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728156842 ; , s. 51-56
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Conference paper (peer-reviewed)abstract
- In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating 'good or better', etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates. © 2020 IEEE.
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| 4. |
- Kelkkanen, Viktor, et al.
(author)
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Bitrate Requirements of Non-Panoramic VR Remote Rendering
- 2020
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In: MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia. - New York, NY, USA : ACM Publications. - 9781450379885
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Conference paper (peer-reviewed)abstract
- This paper shows the impact of bitrate settings on objective quality measures when streaming non-panoramic remote-rendered Virtual Reality (VR) images. Non-panoramic here refers to the images that are rendered and sent across the network, they only cover the viewport of each eye, respectively.To determine the required bitrate of remote rendering for VR, we use a server that renders a 3D-scene, encodes the resulting images using the NVENC H.264 codec and transmits them to the client across a network. The client decodes the images and displays them in the VR headset. Objective full-reference quality measures are taken by comparing the image before encoding on the server to the same image after it has been decoded on the client. By altering the average bitrate setting of the encoder, we obtain objective quality scores as functions of bitrates. Furthermore, we study the impact of headset rotation speeds, since this will also have a large effect on image quality.We determine an upper and lower bitrate limit based on headset rotation speeds. The lower limit is based on a speed close to the average human peak head-movement speed, 360°s. The upper limit is based on maximal peaks of 1080°s. Depending on the expected rotation speeds of the specific application, we determine that a total of 20--38Mbps should be used at resolution 2160×1200@90,fps, and 22--42Mbps at 2560×1440@60,fps. The recommendations are given with the assumption that the image is split in two and streamed in parallel, since this is how the tested prototype operates.
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| 5. |
- Kelkkanen, Viktor, et al.
(author)
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QoE of Frame Stalls in Remote 6-DOF VR
- 2022
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In: <em>International Conference on Quality of Multimedia Experience, QoMEX 2022</em>. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665487948
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Conference paper (peer-reviewed)abstract
- Remotely rendered Virtual Reality (VR) typically has to rely upon less capable and less reliable communication channels as compared to locally rendered VR. Such communication channels pose the risk of impairing the VR experience with temporary disturbances in form of frame stalls that would not be present in local VR. However, it is not obvious to which extent the durations of temporary stalls affect the Quality of Experience (QoE) in interactive 6-degrees-of-freedom (DOF) VR, and neither to which extent present reprojection/warping techniques mitigate any adverse effects of such stalls on user perception.In this paper, subjective experiments were conducted with N =29 users to quantify the relationship between QoE and single, isolated stall durations in interactive 6-DOF remote VR when stalls either freeze the frame in place, or when frames are reprojected by the VR driver. Results indicate that given a 90 fps headset and a simple task that requires moderate head-movement; (1) short, isolated stalls of up to 4 frames go unnoticed on average, no matter whether reprojection is used or not, (2) the number of perceived stalls when using reprojection is the same or lower than the reference (no stall) in the range 1-4 frame stalls, (3) reprojection entails significantly higher user ratings for stalls ranging from 8 frames and beyond, (4) the improvement of using reprojection over freezes in terms of making more stalling events imperceptible is highest in the range 8-16 frames. © 2022 IEEE.
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| 6. |
- Kelkkanen, Viktor, et al.
(author)
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Remapping of hidden area mesh pixels for codec speed-up in remote VR
- 2021
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In: 2021 13th International Conference on Quality of Multimedia Experience, QoMEX 2021. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665435895 ; , s. 207-212
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Conference paper (peer-reviewed)abstract
- Rendering VR-content generally requires large image resolutions. This is both due to the display being positioned close to the eyes of the user and to the super-sampling typically used in VR. Due to the requirements of low latency and large resolutions in VR, remote rendering can be difficult to support at sufficient speeds in this medium.In this paper, we propose a method that can reduce the required resolution of non-panoramic VR images from a codec perspective. Because VR images are viewed close-up from within a headset with specific lenses, there are regions of the images that will remain unseen by the user. This unseen area is referred to as the Hidden-Area Mesh (HAM) and makes up 19% of the screen on the HTC Vive VR headset as one example. By remapping the image in a specific manner, we can cut out the HAM, reduce the resolution by the size of the mesh and thus reduce the amount of data that needs to be processed by encoder and decoder. Results from a prototype remote renderer show that by using the proposed Hidden-Area Mesh Remapping (HAMR), an implementation-dependent speed-up of 10-13% in encoding, 17-18% in decoding and 7-11% in total can be achieved while the negative impact on objective image quality in terms of SSIM and VMAF remains small. © 2021 IEEE.
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| 7. |
- Kelkkanen, Viktor, et al.
(author)
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VRstalls : A Dataset on the QoE of Frame Stalls in 6-DOF VR
- 2022
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In: 2022 14th International Conference on Quality of Multimedia Experience, QoMEX 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665487948
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Conference paper (peer-reviewed)abstract
- A dataset has been created from an experiment regarding the subjective Quality of Experience (QoE) in relation to stalling video feeds in interactive 6-Degrees-of-freedom Virtual Reality (6-DOF VR). The main purpose of collecting this data is to provide insight into the QoE of stalls that may occur in interactive remote-rendered 6-DOF VR. The data contains information regarding user ratings coupled with various stall lengths and for both frozen or reprojected images. Additionally, user motion and pixel changes (in the form of Mean Square Errors) in successive images in the vicinity of the stall are also stored. In summary, 29 users participated in testing, each rating 16 scenes, amounting to a total of 464 rated scenes. © 2022 IEEE.
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| 8. |
- Nawaz, Omer, et al.
(author)
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Impact of Human and Content Factors on Quality of Experience of Online Video Streaming
- 2020
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In: PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS - DCNET, OPTICS, SIGMAP AND WINSYS (ICETE), VOL 2. - : SCITEPRESS. - 9789897584459 ; , s. 59-66
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Conference paper (peer-reviewed)abstract
- Although expensive, but the most reliable measure of user perception is by direct human interaction by taking input from the user about a stimulus quality. In our previous studies, we have identified some subjects getting bored and losing focus by rating lots of video clips of small duration during subjective quality assessments. Moreover, the psychological effects, i.e. user delight, frequency of watching online videos (experience), mood, etc. must not influence the user Mean Opinion Score (MOS) for determining the quality of the shown stimuli. In this paper, we have investigated the impact of user delight, frequency of watching online video content (experience) and different mood levels on MOS for streamed video stimuli in various network conditions by subjective quality assessments. We have observed a slight tendency of better scores when the user likes the stimulus. However, our results show that if the subjective assessments are conducted by carefully following the guidelines, the users impartially rate the video stimuli solely based on the quality artifacts irrespective of their delight towards the shown content. Although, we have observed an effect of user mood on MOS ratings; for almost all the stimuli, but the results suggest the need of more detailed study; i.e. with a large and diverse set of subjects, to obtain significant statistical relevance.
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| 9. |
- Nawaz, Omer, et al.
(author)
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Influence of Gender and Viewing Frequency on Quality of Experience
- 2020
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In: 12th International Conference on Quality of Multimedia Experience, QoMEX 2020. - : IEEE. - 9781728159652
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Conference paper (peer-reviewed)abstract
- Some of the most important aspects for content creators and service providers are the content appeal and the time consumed by the end users on a particular application or service. Gender and age can influence the Quality of Experience (QoE) ratings of multimedia based on the nature of the shown content, yet few studies have quantized this notion. In this paper, we zoom in on the influence of gender on user ratings in a video QoE study (N=89) with packet loss as the main system influence factor. We have analyzed the impact of gender on QoE subjective ratings both as a standalone influence factor and in coherence of temporal traits like the frequency of watching online content. We have observed significant trends to highlight the importance of systematically checking and reporting on the impact of basic human factors, such as gender in relation to quality perception with respect to different types of content. © 2020 IEEE.
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| 10. |
- Singh, Saket Vikram, et al.
(author)
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Quality of Experience of 360-degree Videos Played in Google Cardboard Devices
- 2020
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In: PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS - DCNET, OPTICS, SIGMAP AND WINSYS (ICETE), VOL 2. - : SCITEPRESS. - 9789897584459 ; , s. 115-122
-
Conference paper (peer-reviewed)abstract
- Google Cardboard boxes provide a cost-efficient way to introduce users to Virtual Reality (VR) applications. These devices are suitable to be utilized for entertainment, gaming, and online studies. The 360-degree videos also known as immersive videos, play panoramic view in a video. The videos are played with a mobile phone mounted on a cardboard box and are viewed by wearing or holding the cardboard box. This paper studies the QoE of users (N=60) with QoE features user comfort, presence, and interactivity with panoramic video, based on QoE factors such as lens quality, weight and handling properties of the device. The experimental data is analysed in terms of statistical properties such as Mean Opinion Scores (MOS) including confidence intervals, as well as Percents of Good or Better (%GoB) and Poor or Worse (%PoW). Furthermore, the correlations between user ratings with respect to different groups of QoE features are investigated. Overall, the paper shows cardboard boxes to yield good-to-fair QoE for viewing panoramic videos.
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