Welcome to Mobile Content Quality of Experience on - MobConQoE '07

2007 ◽  
Keyword(s):  
Quality Of Experience ◽  
Content Quality

Emotional Aspects and Quality of Experience for Multifactor Evaluation of Audiovisual Content

2014 ◽  
Vol 2 (4) ◽  
pp. 40-61 ◽  
Author(s):  
George Kalliris ◽  
Maria Matsiola ◽  
Charalampos Dimoulas ◽  
Andreas Veglis
Keyword(s):  
Quality Of Experience ◽  
Communication Process ◽  
Emotional Impact ◽  
Mediated Learning ◽  
Audiovisual Communication ◽  
Mediated Communication ◽  
Content Quality ◽  
Communication Efficiency ◽  
Emotional Aspects

The present paper investigates multifactor audiovisual-content quality evaluation strategies, in mediated communication. The primary aims of the work are to identify, describe and model the mechanisms that the attributes of source content and its encoding properties influence the communication process and the involved emotional aspects in terms of Quality of Experience (QoE), information perception and understanding. Mediated learning constitutes a demanding thus suitable investigation case-study, where communication efficiency can be monitored with the use of applicable Quality of Learning (QoL) parameters, such as the learning outcome and its relation to the prior knowledge status. Real-world e-learning scenarios are utilized for sentiment analysis tests combined with QoE/QoL evaluation, using both subjective scores and perceptually-adapted metrics. This experimental research attempts to monitor communication efficiency and its relation to the quality and emotional impact of the mediated content, offering new insights in mediated learning and broader audiovisual communication services.

scholarly journals Investigation of Quality of Experience Using ECG Features during Consumption of Audiovisual Content

2017 ◽  
Vol 9 (3) ◽  
pp. 340-344
Author(s):  
Vytautas Abromavičius
Keyword(s):  
Heart Rate ◽  
Quality Of Experience ◽  
Subjective Evaluation ◽  
Subjective Assessment ◽  
Video Content ◽  
Content Quality ◽  
Video Recordings ◽  
Multimedia Technologies ◽  
To Receive

Development of new multimedia technologies allows us to receive better quality of audio and video content. Quality of Experience (QoE) evaluates given content from the consumer’s perspective. This measurement allows to evaluate not only visual and audible quality, but also general acceptability of provided service. QoE evaluation is getting popular between engineers, designers, retailers who wants to provide high quality content for consumers. QoE is generally evaluated subjectively by surveys. It is possible to find relationship between physiological signals measured while user is consuming audiovisual content and make the subjective evaluation of this experience. This paper investigates relationship between heart rate and QoE while user is watching 1 min duration video recordings on three different devices. Heart rate was calculated as mean RR interval for each recording. Mean RR intervals of 0.848 s, 0.869 s and 0.884 s were calculated for low, medium and high QoE device configurations, respectively. ANOVA analysis results indicates a relation between heart rate and QoE level. The results can help to develop further the investigations of QoE level and heart rate relationship for various subjective assessment, device configurations and content provided.

scholarly journals Memories of Video: Impact of Sequencing on Rated Technical Quality for Viewed and Visualized Disruptions

2019 ◽  
Vol 63 (1) ◽  
pp. 386-390
Author(s):  
Thomas He ◽  
Chelsea DeGuzman ◽  
Leon Zucherman ◽  
Tiffany Tong ◽  
Mark Chignell
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Streaming Video ◽  
Technical Quality ◽  
End Effect ◽  
Content Quality ◽  
Relative Change ◽  
The Impact

In this paper we explore how memories of experience with streaming video affect Quality of Experience (QoE) indicators that are of interest to service providers and marketers. Since observations of experience are time consuming, and the effects of technical quality (TQ) are difficult to entangle from content quality (CQ), we examined the impact of a visualization methodology for assessing experiences. A study was carried out to examine how well overall technical quality (TQ) judgments for a sequence of visualized video experience (a picture of a red video playbar with yellow portions indicating disrupted video in place of actually viewed video) would correspond to overall TQ judgments made after watching a sequence of actual videos. Sequencing effects found in overall TQ ratings, made after viewing visualizations (with their overlaid disruptions) were similar to sequencing effects found after viewing actual videos. However, the sequencing effects after viewing the visualizations were less pronounced than the corresponding sequencing effects that were found after viewing actual videos. Sequences of both visualized and actually viewed videos showed significant negative end effect and trend effects (both positive and negative). There was also evidence that sequencing effects respond to relative change in TQ rather than absolute TQ.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

Evaluation of the Content Quality of YouTube Videos on Alternate Therapies for Prevention or Treatment of COVID-19

2021 ◽  
Vol 25 (2) ◽  
pp. 133-155
Author(s):  
Sarvesh Sawant ◽  
Aswathi Nair ◽  
Shaik Aisha Sultana ◽  
Arjun Rajendran ◽  
Kapil Chalil Madathil
Keyword(s):  
Content Quality ◽  
Youtube Videos

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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