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to be achieved in order to provide very satisfactory experience to user as shown in Figure 13. 2.3. Objective Assessment Techniques for Objective QoE Factors The application of psychological models for understanding human intentions and behaviot Viean Opinion Score (MOS).The E-model is combines a number of different impairments to

Figure 13 to be achieved in order to provide very satisfactory experience to user as shown in Figure 13. 2.3. Objective Assessment Techniques for Objective QoE Factors The application of psychological models for understanding human intentions and behaviot Viean Opinion Score (MOS).The E-model is combines a number of different impairments to

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Figure 1: Diagramme conceptuel de QoE et facteurs d'influence entre l'humain, la technologie, des affaires et le contexte ainsi que leur effet sur la QoE.
Figure 1: Diagramme conceptuel de QoE et facteurs d'influence entre l'humain, la technologie, des affaires et le contexte ainsi que leur effet sur la QoE.
1. Proposer un modéle holistique QoE: Dans la premiére étape, il est important de comprendre
1. Proposer un modéle holistique QoE: Dans la premiére étape, il est important de comprendre
ariables de résultats [67] [49] [48]. De méme, nous divisons tous les facteurs en trois catégorie:
ariables de résultats [67] [49] [48]. De méme, nous divisons tous les facteurs en trois catégorie:
Figure 4. Interaction interdomaine rincipales: i) les facteurs de prédiction (ii) les facteurs résultats et les facteurs de modération (iii)
Figure 4. Interaction interdomaine rincipales: i) les facteurs de prédiction (ii) les facteurs résultats et les facteurs de modération (iii)
Mes papiers IV, V, VI et VII sont mes contributions liées a ce travail. Tableau 1: Résumé de la phase 2 de contribution
Mes papiers IV, V, VI et VII sont mes contributions liées a ce travail. Tableau 1: Résumé de la phase 2 de contribution
Figure 1: Conceptual diagram of QoE and Influencing factors interaction between human, technology, business and context as well as their effect on QoE.
Figure 1: Conceptual diagram of QoE and Influencing factors interaction between human, technology, business and context as well as their effect on QoE.
The contributions of this thesis could be broadly divided into three parts as show in
The contributions of this thesis could be broadly divided into three parts as show in
Table 2: QoE and HCI
Table 2: QoE and HCI
Table 3: Comparison of various disciplines w.r.t QoE
Table 3: Comparison of various disciplines w.r.t QoE
human behavior. REIDY BOY MEM AY BEV ARR NONE AER UE DEERE AME MINI LOUGLIN IIS ON influence of various factors (attitude, social norms, PBC) over human behavior. Wanmin Wu et al. in [52] propose the use of TAM model as QoE construct in distributed interactive multimedia environment. In [53] author uses a TAM model for pervasive computing to understand human behavior towards adoption of pervasive computing. Psychological models such as TAM, TPB, and UTAUT are more focused on understanding human external factors (social norms) and control factors (price, complexity of system etc) to get precise
human behavior. REIDY BOY MEM AY BEV ARR NONE AER UE DEERE AME MINI LOUGLIN IIS ON influence of various factors (attitude, social norms, PBC) over human behavior. Wanmin Wu et al. in [52] propose the use of TAM model as QoE construct in distributed interactive multimedia environment. In [53] author uses a TAM model for pervasive computing to understand human behavior towards adoption of pervasive computing. Psychological models such as TAM, TPB, and UTAUT are more focused on understanding human external factors (social norms) and control factors (price, complexity of system etc) to get precise
Figure 5: Proposed High level Communication Eco system technology, business, context and human. and non living (technology, and business) in a particular context”. A conceptual diagram of < interaction represents in-service user experience. Various technological aspects such as servic
Figure 5: Proposed High level Communication Eco system technology, business, context and human. and non living (technology, and business) in a particular context”. A conceptual diagram of < interaction represents in-service user experience. Various technological aspects such as servic
Figure 6: High Level Diagram for QoE Interaction in Communication Ecosystem three levels of abstraction: entity, roles, and attributes/characteristics. An entity is a real-world
Figure 6: High Level Diagram for QoE Interaction in Communication Ecosystem three levels of abstraction: entity, roles, and attributes/characteristics. An entity is a real-world
User annoyance is not solely caused by decline in QoS of a VoD service. For example.
User annoyance is not solely caused by decline in QoS of a VoD service. For example.
Table 5: Comparison between our proposal models with respect to other models main points in order to compare them with our proposed QoE frame work.
Table 5: Comparison between our proposal models with respect to other models main points in order to compare them with our proposed QoE frame work.
interactive data as shown in Figure 8.
interactive data as shown in Figure 8.
requirements, as shown in Figure 9. Time dependence and symmetry are two important aspects on understanding the categories Multimedia services can be subdivided based on their temporal and data symmetry of multimedia services.
requirements, as shown in Figure 9. Time dependence and symmetry are two important aspects on understanding the categories Multimedia services can be subdivided based on their temporal and data symmetry of multimedia services.
Table 6: QoS for Multimedia Services ITU-T G.1010)
Table 6: QoS for Multimedia Services ITU-T G.1010)
of QoE as given in Figure 10 4.2.1. Subjective Assessment Method:
of QoE as given in Figure 10 4.2.1. Subjective Assessment Method:
categories the ratio of positive to negative comments and produces results in histogram format: QoE relationship based on both quantitative and qualitative assessment.
categories the ratio of positive to negative comments and produces results in histogram format: QoE relationship based on both quantitative and qualitative assessment.
same across the levels of the variables.
same across the levels of the variables.
parametric statistics is used for interval scales, thus the use of Pearson correlation
parametric statistics is used for interval scales, thus the use of Pearson correlation
Table 9: Human Physiological and Cognitive factors cognitive estimates are presented in [106] and they are summarized in Table 9.
Table 9: Human Physiological and Cognitive factors cognitive estimates are presented in [106] and they are summarized in Table 9.
Table 10: Response time of webpage download and Telephony vs. Human reaction accomplish within duration ¢ and once they finish their task, their performance is analyzed on the
Table 10: Response time of webpage download and Telephony vs. Human reaction accomplish within duration ¢ and once they finish their task, their performance is analyzed on the
this study to see if different content types produce similar or different user experience. Figure 14: Research Model of Video Streaming Study Kesearch model presents prediction, outcome and moderation factors (refer rigure 14). In this user study, the prediction factors or influencing factors are three Network QoS (NQoS) parameters and one Application QoS (AQoS) parameter. The objective is to assess the combined effect of prediction factors over perceived video quality. Perceived Video Quality (PVQ) is QoE metric which represents user perception about the quality of a video clip. During user study, this QoE factor is collected on the basis of user ratings/scores (quantitative process) and user comments (qualitative process). The type of video content is considered as moderation factor in Py ee a a: ey nn. ce Rl b,j, i ny i a ec ne Ce fs i eee, tee
this study to see if different content types produce similar or different user experience. Figure 14: Research Model of Video Streaming Study Kesearch model presents prediction, outcome and moderation factors (refer rigure 14). In this user study, the prediction factors or influencing factors are three Network QoS (NQoS) parameters and one Application QoS (AQoS) parameter. The objective is to assess the combined effect of prediction factors over perceived video quality. Perceived Video Quality (PVQ) is QoE metric which represents user perception about the quality of a video clip. During user study, this QoE factor is collected on the basis of user ratings/scores (quantitative process) and user comments (qualitative process). The type of video content is considered as moderation factor in Py ee a a: ey nn. ce Rl b,j, i ny i a ec ne Ce fs i eee, tee
Table 11: Test Setup Table In this section we provide quantitative and qualitative assessment of user study and discuss
Table 11: Test Setup Table In this section we provide quantitative and qualitative assessment of user study and discuss
Table 12: Experimental Data (Raw Decision Table) decision attributes describe the user scores.
Table 12: Experimental Data (Raw Decision Table) decision attributes describe the user scores.
Table 13: Discretized Data Table Step 2: Classify and reduce attribute set. The second step is to reduce and classify data.
Table 13: Discretized Data Table Step 2: Classify and reduce attribute set. The second step is to reduce and classify data.
Table 14: Discretized Data Table Step 2. Classify and reduce attribute set. For football video, using RST, a core set was found to be Core= {Packet Loss, Video Bit rate} as show in Figure 18. It means packet loss and video bit rate are two key influencing factors, while delay and packet reordering don’t have significant impact on user perceived video quality. reduced into three levels (1.e., 3= User Acceptance, 2=Normal/Fair, 1=User Rejection). Table 14
Table 14: Discretized Data Table Step 2. Classify and reduce attribute set. For football video, using RST, a core set was found to be Core= {Packet Loss, Video Bit rate} as show in Figure 18. It means packet loss and video bit rate are two key influencing factors, while delay and packet reordering don’t have significant impact on user perceived video quality. reduced into three levels (1.e., 3= User Acceptance, 2=Normal/Fair, 1=User Rejection). Table 14
were arranged in following categories. 2. Neutral Comments: Neutral comments reflect neutral opinions such as “Normal
were arranged in following categories. 2. Neutral Comments: Neutral comments reflect neutral opinions such as “Normal
Exp.5, as poor and bad.
Exp.5, as poor and bad.
Table 15: Comparison between PSTN and VoIP STN telephony was designed to offer real time telephony service, however internet only
Table 15: Comparison between PSTN and VoIP STN telephony was designed to offer real time telephony service, however internet only
Figure 24: Classification of Call Setup Faults Incoming Call Setup Faults (SCF) and their classifications are given below (see Figure 24). There are two types of call setup faults (1) Outgoing Call Setup Faults (OCSF) anc
Figure 24: Classification of Call Setup Faults Incoming Call Setup Faults (SCF) and their classifications are given below (see Figure 24). There are two types of call setup faults (1) Outgoing Call Setup Faults (OCSF) anc
on delay and the strength of the reflected signals. In analogue PSTN telephony, the main source
on delay and the strength of the reflected signals. In analogue PSTN telephony, the main source
Figure 26: (a). Impairment Scale for often category (b) for never category 6.4.2.2 Customer preferences
Figure 26: (a). Impairment Scale for often category (b) for never category 6.4.2.2 Customer preferences
Table 16: Perceived Availability versus Call Setup Faults Table 16 shows the customer data result for call signaling quality.
Table 16: Perceived Availability versus Call Setup Faults Table 16 shows the customer data result for call signaling quality.
Table 17: Perceived Call Quality versus Technical Faults any technical issue. Customer’s perception about call quality is affected by various technical anc
Table 17: Perceived Call Quality versus Technical Faults any technical issue. Customer’s perception about call quality is affected by various technical anc
Table 18: Comparative Analysis of PSTN and VoIP Faults
Table 18: Comparative Analysis of PSTN and VoIP Faults
Table 19: Customer Preferences based on Customer Handset failure. Preference 2 is having smooth voice quality; Preference 3 is HD ring tone.
Table 19: Customer Preferences based on Customer Handset failure. Preference 2 is having smooth voice quality; Preference 3 is HD ring tone.
Figure 27: Customer Preferences based on Customer Telephone Handset
Figure 27: Customer Preferences based on Customer Telephone Handset
Table 20: Overall Satisfaction differentiated based on Customer Age
Table 20: Overall Satisfaction differentiated based on Customer Age
supplying local virtual environments and modifying the position of the local as well as of the nhanced by a plug-in to control the virtual environment in order to support QoE requirements.
supplying local virtual environments and modifying the position of the local as well as of the nhanced by a plug-in to control the virtual environment in order to support QoE requirements.
QoE with 3D virtual acoustic environment as shown in Figure 29. To evaluate 3D Audio Teleconferencing tool and analyze the impact of virtual acoustic environment over users, we present a research model for 3D Telephony. From high level QoE interaction model (chapter 3), we see the interaction between QoE-&-technology, QoE-&- business and QoE-&-context domains. In the current contribution, we concentrate to study the relationship between QoE-&-context domains. More specifically we try to compare and correlate QNAD xewxrith QT <criaetiial annnirntin nnrnvennmant aa nhanrmn sen TD nem VW
QoE with 3D virtual acoustic environment as shown in Figure 29. To evaluate 3D Audio Teleconferencing tool and analyze the impact of virtual acoustic environment over users, we present a research model for 3D Telephony. From high level QoE interaction model (chapter 3), we see the interaction between QoE-&-technology, QoE-&- business and QoE-&-context domains. In the current contribution, we concentrate to study the relationship between QoE-&-context domains. More specifically we try to compare and correlate QNAD xewxrith QT <criaetiial annnirntin nnrnvennmant aa nhanrmn sen TD nem VW
system. computer using a specially designed user interface (as shown in Figure 30) on Linux operati recommendations [77] as far as possible. All tests were conducted in a quiet listening room on a
system. computer using a specially designed user interface (as shown in Figure 30) on Linux operati recommendations [77] as far as possible. All tests were conducted in a quiet listening room on a
yerformance but it has the lowest localization easiness, spatial audio quality and overall
yerformance but it has the lowest localization easiness, spatial audio quality and overall
Figure 31(b). Subjective QoE vs Virtual Room Size
Figure 31(b). Subjective QoE vs Virtual Room Size
3 Experiment IIT: QoE Moderation based on Gender values with mixed gender voice type since both voices can be distinguished more easily than the In experiment 7.5.2.1, It was discussed that how the size of virtual room could impact various Results show that both localization performance and localization easiness obtain the highest
3 Experiment IIT: QoE Moderation based on Gender values with mixed gender voice type since both voices can be distinguished more easily than the In experiment 7.5.2.1, It was discussed that how the size of virtual room could impact various Results show that both localization performance and localization easiness obtain the highest
concurrent voices of the same gender.
concurrent voices of the same gender.
Table 24: Analysis of Human QoE Factors in relation to Virtual Acoustic Environment performance increases for male participants as the size of virtual room decreases. The overall
Table 24: Analysis of Human QoE Factors in relation to Virtual Acoustic Environment performance increases for male participants as the size of virtual room decreases. The overall
From Table 23, values for x (successful participants) and n (total numbers of participants) are Table 23: VAE and successful & unsuccessful gender groups of test participants
From Table 23, values for x (successful participants) and n (total numbers of participants) are Table 23: VAE and successful & unsuccessful gender groups of test participants
room bring no considerable difference in their localization performance. participants’ perform better localization in middle size room (15 m3), while the small and big size
room bring no considerable difference in their localization performance. participants’ perform better localization in middle size room (15 m3), while the small and big size
Figure 34: LE for male and female participants in virtual acoustic rooms female participants also keep different perceptual levels. Localization Performance vs. Localization Easiness:
Figure 34: LE for male and female participants in virtual acoustic rooms female participants also keep different perceptual levels. Localization Performance vs. Localization Easiness:
Figure 35: Basic Functions of QOM degradation in QoE score.
Figure 35: Basic Functions of QOM degradation in QoE score.
Table 25: Comparision of different Video Quality tools with QOM
Table 25: Comparision of different Video Quality tools with QOM
components of the framework. provides the VoD service over the client web browser. All the objects of the framework ar A web-based client interface is developed to facilitate users to watch videos online and giv
components of the framework. provides the VoD service over the client web browser. All the objects of the framework ar A web-based client interface is developed to facilitate users to watch videos online and giv
Figure 37: Screen shot for Web based client Interface number. And S/he has to insert client IP address before processing video test. A client comes to the VoD service web page and chooses the video contents and it
Figure 37: Screen shot for Web based client Interface number. And S/he has to insert client IP address before processing video test. A client comes to the VoD service web page and chooses the video contents and it
QoE Framework Following Figure 38 shows the record of QoE and QoS information.
QoE Framework Following Figure 38 shows the record of QoE and QoS information.
Figure 39: Database structure comments. User information will be stored in the database for further analysis.
Figure 39: Database structure comments. User information will be stored in the database for further analysis.
Figure 44: Architecture of the Android application for QOE measurement interaction with the end-user and with the remote multimedia service provider (MSP). The Manager (MC) is the main component responsible for interacting with the outside world (user and service provider) and managing rest of the system components.
Figure 44: Architecture of the Android application for QOE measurement interaction with the end-user and with the remote multimedia service provider (MSP). The Manager (MC) is the main component responsible for interacting with the outside world (user and service provider) and managing rest of the system components.
Figure 45: Screenshots from the QoE measurement application
Figure 45: Screenshots from the QoE measurement application
Figure 46: Cost function graphs of two methods
Figure 46: Cost function graphs of two methods
7. Conclusion
7. Conclusion
Experiment 1: Pls give your opinion about current video experience. (In English or
Experiment 1: Pls give your opinion about current video experience. (In English or
Q1 Par rapport au fonctionnement de votre téléphone par internet / ligne fixe, vous diriez que vous étes:
Q1 Par rapport au fonctionnement de votre téléphone par internet / ligne fixe, vous diriez que vous étes:
Related topics:
Computer ScienceVoice over IPQuality of Experience
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