No-Reference Quality Assessment of Screen Content Pictures

Gu, Ke; Zhou, Jun; Qiao, Junfei; Zhai, Guangtao; Wang, Lin; Bovik, Alan C.

doi:10.1109/tip.2017.2711279

Cited by 235 publications

(86 citation statements)

References 60 publications

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“…Other standards, such as Simultaneous Double Stimulus for Continuous Evaluation, Subjective Assessment Methodology for Video Quality, Degradation Category Rating or DoubleStimulus Impairment Scale and Comparison Category Rating, are discussed in [27].…”

Section: A Subjective Assessmentmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and addedvalue services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

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Section: A Subjective Assessmentmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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“…Perceived video quality depends largely on the type of video content [27][28][29][30]. Hence, while selecting the reference sequences, spatial information (SI) and temporal information (TI) are two critical factors that must be taken into account.…”

Section: Overall Experiments Designmentioning

confidence: 99%

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Pal

Triyason

2018

International Journal of Digital Multimedia Broadcasting

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Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

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“…The perceived video quality depends on the type of video content to a great extent which has been discussed by authors in [32,[48][49][50]. To define the different types of video contents we have considered the Spatial Information (SI) and Temporal Information (TI) of the source videos.…”

Section: Application Qos Factorsmentioning

confidence: 99%

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

Pal

Vanijja

2017

Advances in Multimedia

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We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.

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No-Reference Quality Assessment of Screen Content Pictures

Cited by 235 publications

References 60 publications

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

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