Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services

Yang, Fuzheng; Song, Jiarun; Wan, Shuai; Wu, Hong Ren

doi:10.1109/jstsp.2012.2207705

Cited by 34 publications

(20 citation statements)

References 32 publications

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“…A content-adaptive packet layer model to assess the QoE in terms of the frame rate quality for video services using RTP/UDP was presented in [100]. The bit-rate, packet loss rate, temporal complexity (the acuteness of temporal changes of a video sequence), and the frame type information of the transported video were determined from the packet headers.…”

Section: Estimating the Qoe Using Predictive Modelsmentioning

confidence: 99%

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

Juluri

Tamarapalli

Medhi

2016

IEEE Commun. Surv. Tutorials

198

104

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Video on demand streaming (VoD) services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017 [1]. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step towards providing a satisfactory service is to be able to quantify the users' perceptions of the current service level. Quality of Experience (QoE) is a quality metric that provides a holistic measure of the users perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services. Index Terms-Quality of Experience (QoE), video streaming, Video On Demand (VoD), measurement, online video1553-877X (c)

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Section: Estimating the Qoe Using Predictive Modelsmentioning

confidence: 99%

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

Juluri

Tamarapalli

Medhi

2016

IEEE Commun. Surv. Tutorials

198

104

View full text Add to dashboard Cite

show abstract

“…Bitstreamlayer [16][17][18] Packet-layer [19,20] Hybrid [21][22][23][24][25][26][27] For example, similarity structural (SSIM) is an engineering approach, but many variations of SSIM also incorporate psychophysical features in the design. In this case, we still classify these variations as engineering approach as their major basis is SSIM.…”

Section: Objective Quality Modelmentioning

confidence: 99%

From QoS to QoE: A Tutorial on Video Quality Assessment

Chen

Zhang

2015

IEEE Commun. Surv. Tutorials

288

124

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Quality of Experience (QoE) is the perceptual Quality of Service (QoS) from the users' perspective. For video service, the relationship between QoE and QoS (such as coding parameters and network statistics) is complicated because users' perceptual video quality is subjective and diversified in different environments. Traditionally, QoE is obtained from subjective test, where human viewers evaluate the quality of tested videos under a laboratory environment. To avoid high cost and offline nature of such tests, objective quality models are developed to predict QoE based on objective QoS parameters, but it is still an indirect way to estimate QoE. With the rising popularity of video streaming over the Internet, data-driven QoE analysis models have newly emerged thanks to availability of large-scale data. In this article, we give a comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages and drawbacks. We also introduce QoE-based video applications, and finally identify the future research directions of QoE.Index Terms-Quality of Experience, Subjective Test, Objective Quality Model, Data-driven Analysis 1553-877X (c)

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“…By the same research group, the packet layer model presented in [146] uses video resolution, bitrate, packet loss rate, and some information of the codec settings to design a quality estimator for H.264/AVC-and MPEG-2-based encoded videos. An improvement on such statistical parameters-based models is found in [147] where temporal and spatial characteristics of a video are estimated from the packet header to build a content-adaptive model for quality assessment. The no-reference method presented in [148] is based on a nonlinear relationship between an objective quality metric and the quality-related parameters.…”

Section: Parametric Packet-layer Modelmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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Content-Adaptive Packet-Layer Model for Quality Assessment of Networked Video Services

Cited by 34 publications

References 32 publications

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

From QoS to QoE: A Tutorial on Video Quality Assessment

No-reference image and video quality assessment: a classification and review of recent approaches

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