A_PSQA: Efficient real-time video streaming QoE tool in a future media internet context

Cherif, Wael; Ksentini, Adlen; Negru, Daniel; Sidibe, M.

doi:10.1109/icme.2011.6011993

Cited by 25 publications

(11 citation statements)

References 8 publications

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“…For example, these existing approaches aim to map QoS to QoE [10] or to predict the QoE [11] [12] with a main focus on audio-visual services and do not take into account additional assets such as sensory effects. Other QoE models such as the one presented in [13] are based on perception, emotion, and sensation and mainly address adaptation and presentation issues without explicitly addressing sensory effects.…”

Section: Introductionmentioning

confidence: 99%

A utility model for sensory experience

Timmerer

Rainer

Waltl

2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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Enriching multimedia with additional effects such as olfaction, light, wind, or vibration is gaining more and more momentum in both research and industry. Hence, there is the need to determine the influence of individual effects on the Quality of Experience (QoE). In this paper, we present a subjective quality assessment using the MPEG-V standard to annotate video sequences with individual sensory effects (i.e., wind, light, and vibration) and all combinations thereof. Based on the results we derive a utility model for sensory experience that accounts for the assessed sensory effects. Finally, we provide an example instantiation of the utility model and validate it against current and past results of our subjective quality assessments conducted so far.

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Section: Introductionmentioning

confidence: 99%

A utility model for sensory experience

Timmerer

Rainer

Waltl

2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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“…• Objective content quality level (media parameters): these are some parameters related to the actual media encoding algorithms and mechanisms that can provide information on the actual content presented to the user [10]. They are briefly summarized in Table 1.…”

Section: Qoe Model For Dashmentioning

confidence: 99%

Automated QoE evaluation of Dynamic Adaptive Streaming over HTTP

Alberti

Renzi

Timmerer

et al. 2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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Dynamic Adaptive Streaming over HTTP (DASH) is referred to as a multimedia streaming standard to deliver high quality multimedia content over the Internet using conventional HTTP Web servers. As a fundamental feature, it enables automatic switching of quality levels according to network conditions, user requirements, and expectations. Currently, the proposed adaptation schemes for HTTP streaming mostly rely on throughput measurements and/or buffer-related metrics, such as buffer exhaustion and levels. In this paper, we propose to enhance the DASH adaptation logic by feeding it with additional information from our evaluation of the users' perception approximating the userperceived quality of video playback. The proposed model aims at conveniently combining TCP-, buffer-, and media content-related metrics as well as user requirements and expectations to be used as an input for the DASH adaptation logic. Experiments have demonstrated that the chosen model enhances the capability of the adaptation logic to select the optimal video quality level. Finally, we integrated all our findings into a real DASH system with QoE monitoring capabilities.

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“…The non-linear relationship between the video bitrate and the perceived quality by the user is stated in [36]. In [3], a No-Reference QoE assessment measure, A-PSQA, is proposed as a performance evaluation method, based on the analysis with respect to the packet-loss percentage. Zinner et al [65] stated that the Structural Similarity Index Model (SSIM) can be used to quantify the influence of packet loss on the QoE; while the Peak Signal-Noise Ratio (PSNR) can not.…”

Section: Related Work On Influential Metricsmentioning

confidence: 99%

“…User rating buttons (1,2,3,4,5) The user is instructed to press one of the horizontally aligned five (based on ACR scale) user rating (UR) buttons (see Fig. 5c) whenever the user feels like rating the quality.…”

Section: User Interfacementioning

confidence: 99%

VLQoE: Video QoE instrumentation on the smartphone

Ickin

Fiedler

Wac

et al. 2014

Multimed Tools Appl

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The usage of network-demanding applications is growing rapidly such as video streaming on mobile terminals. However, network and/or service providers might not guarantee the perceived quality for video streaming that demands high packet transmission rate. In order to satisfy the user expectations and to minimize user churn, it is important for network operators to infer the end-user perceived quality in video streaming. Today, the most reliable method to obtain end-user perceived quality is through subjective tests, and the preferred location is the user interface as it is the closest point of application to the enduser. The end-user perceived quality on video streaming is highly influenced by occasional freezes; technically the extraordinary time gaps between two consecutive pictures that are displayed to the user, i.e., high inter-picture time. In this paper, we present a QoE instrumentation for video streaming, VLQoE. We added functionality to the VLC player to record Multimed Tools Appl a set of metrics from the user interface, application-level, network-level, and from the available sensors of the device. To the best of our knowledge, VLQoE is the first tool of its kind that can be used in user experiments for video streaming. By using the tool, we present a two state model based on the inter-picture time, for the HTTP-and RTSP-based video streaming via 3.5G. Next, we studied the influence of inter-picture time on the user perceived quality through out a user study. We investigated the minimum user perceived inter-picture time, and the user response time.

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A_PSQA: Efficient real-time video streaming QoE tool in a future media internet context

Cited by 25 publications

References 8 publications

A utility model for sensory experience

A utility model for sensory experience

Automated QoE evaluation of Dynamic Adaptive Streaming over HTTP

VLQoE: Video QoE instrumentation on the smartphone

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