A subjective &amp;#x2014; VoIP quality estimation model for G.729 based on native Thai users

Khitmoh, Nalakkhana; Wuttidittachotti, Pongpisit; Daengsi, Therdpong

doi:10.1109/icact.2014.6778920

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Some of them were classified as outliers and were discarded, respectively. The results from conversation-opinion tests with both codecs, which have been previously presented in [63,64], are shown in Tables 4 and 5. Then, following the regressionbased technique as used in [65], the validated data will be used to find the mathematical model and its coefficients using the surface fitting tool in Matlab, as the results for G.711 and G.729 shown in Figs.…”

Section: Finding Mathematical Model Using Surface Fittingmentioning

confidence: 98%

VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

Daengsi¹,

Khitmoh

Wuttidittachotti

2015

Multimedia Systems

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Section: Finding Mathematical Model Using Surface Fittingmentioning

confidence: 98%

VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

Daengsi¹,

Khitmoh

Wuttidittachotti

2015

Multimedia Systems

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“…For example, Cano and Cerdan [53] conducted a subjective study of Skype performance with real calls, but with a different purpose of comparing multiple VoIP applications. Khitmoh et al [54] and Daengsi et al [55] performed subjective studies on VoIP service where every two subjects had a 3-5 minute conversation in a controlled laboratory setup to develop a model for VoIP quality evaluation for the Thai language.…”

Section: Related Workmentioning

confidence: 99%

Methods for Objective and Subjective Evaluation of Zero-Client Computing

et al. 2019

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Zero clients are hardware-based devices without a central processing unit (CPU) that deliver virtual desktops (VDs) from remote computing systems to users. We measured the performance of applications accessed through zero clients to study the feasibility of using this approach to provide a desktop-pc experience across a network. Performance evaluation is complicated because monitoring software cannot be downloaded to the zero clients. Therefore, we introduce a new methodology and metric to measure zero-client VD performance that is based on network-traffic analysis. We conducted objective and subjective studies to determine the sensitivity of application-specific metrics to different network conditions. The results show that the packet loss rate (PLR) impacts zero-client performance for some applications such as video streaming. Subjective tests showed a greater user sensitivity to the PLR for video streaming than for image viewing or Skype. A strong correlation was found between the objective and subjective measurements but the rate at which these measurements changed with increasing PLR differed depending on the application. INDEX TERMS Edge-cloud, measurements, objective study, QoE, remote desktops, subjective study, VDI, VDI metrics, virtual desktops, zero clients. I. INTRODUCTION Virtual-Desktop (VD) technologies have been growing in popularity as several remote desktop tools, such as VNC, Microsoft Remote Desktop Services (RDS), and TeamViewer, have been developed to support this paradigm. Remote desktop clients have been implemented in custom Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs). For example, Teradici Tera2321, is used in equipment called zero clients, which include interfaces such as USB, DVI, and HDMI ports to connect keyboard, video, mouse (KVM) terminals, and an Ethernet port for network connectivity. Zero-client hardware has primarily been developed for enterprises, with each user being supported by one Virtual Machine (VM) in an edge-or commercial-cloud host. Thus, zero clients are used in schools, hospitals, libraries, and businesses [1]-[4]. Zero clients have also been used for highperformance graphics applications to allow multiple users to share the more expensive processing engines [5]. Graphics applications frequently exploit co-location of zero clients The associate editor coordinating the review of this manuscript and approving it for publication was Haibo Wu.

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“…1. Examples of random packet (the lower) and bursty packet loss (the upper), adopted from [24] Previously, the subjective MOS prediction models based on native Thai users referring to delay effects and/or loss effects were proposed in [13,39,41]. Its concept is to gather scores from voting by a group of participants or subjects after VoIP quality testing under loss and/re delay scenarios.…”

Section: Subjective Quality Measurement and Subjective Mos Prediction...mentioning

confidence: 99%

QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

Daengsi

Wuttidittachotti

2019

J Netw Syst Manage

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This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a subjective MOS prediction model based on native Thai users, who use the Thai-tonal language. Then, the different results from the simplified E-model and subjective MOS prediction model were used to create the Bias function, before adding to the simplified E-model. Finally, it has been found that the outputs from the enhanced simplified E-model for the G.729 codec shows better accuracy when compared to the original simplified E-model, specially, after the enhanced model has been evaluated with 4 test sets. The major contribution of this enhancement is that errors are reduced by 58.87 % when compared to the generic simplified E-model. That means the enhanced simplified E-model as proposed in this study can provide improvement beyond the original simplified one significantly.

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A subjective — VoIP quality estimation model for G.729 based on native Thai users

Cited by 9 publications

References 18 publications

VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

Methods for Objective and Subjective Evaluation of Zero-Client Computing

QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

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