A study on the effects of quality of service parameters on perceived video quality

Paudyal, Pradip; Battisti, Federica; Carli, Marco

doi:10.1109/euvip.2014.7018383

Cited by 26 publications

(20 citation statements)

References 7 publications

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“…As expected, transmission impairments have a significant impact on the perceived quality [31]. The perceived quality decreases significantly for high values of jitter and PLR artifacts.…”

Section: Impact Of Key Impairmentsmentioning

confidence: 68%

“…Impact of the number of pauses, their duration and temporal location in TCP transmission protocol is studied in [34] and a new QoE metric for video streaming services is proposed. The network domain parameters: packet loss, delay, jitter, and packet reorder affect the video quality more than video content, though the effect of noise factors like motion, complexity and location also have a significant impact on the perceived quality [10,11,18,31].…”

Section: Related Workmentioning

confidence: 99%

“…To this aim, based on the purpose of the study, a recently proposed video quality database, ReTRiEVED [31], with large number of videos and opinion scores collected from a significant number of subjects, has been used. The rest of paper is organized as follows: Section 2 summarizes the state-of-the-art works related to this contribution, Section 3 briefly introduces the database, in Section 4 the data processing, the video content characterization tools and techniques are presented, results and comments are included in Section 5, and in Section 6 the conclusions are drawn.…”

Section: Introductionmentioning

confidence: 99%

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Impact of video content and transmission impairments on quality of experience

Paudyal

Battisti

Carli

2016

Multimed Tools Appl

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The analysis of the impact of video content and transmission impairments on Quality of Experience (QoE) is a relevant topic for the robust design and adaptation of multimedia infrastructures, services, and applications. The goal of this paper is to study the impact of video content on QoE for different levels of impairments. In more details, this contribution aims at i) the study of the impact of delay, jitter, packet loss, and bandwidth on QoE, ii) the analysis of the impact of video content on QoE, and iii) the evaluation of the relationship between content related parameters (spatial-temporal perceptual information, motion, and data rate) and the QoE for different levels of impairments.

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“…As expected, transmission impairments have a significant impact on the perceived quality [31]. The perceived quality decreases significantly for high values of jitter and PLR artifacts.…”

Section: Impact Of Key Impairmentsmentioning

confidence: 68%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of video content and transmission impairments on quality of experience

Paudyal

Battisti

Carli

2016

Multimed Tools Appl

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“…These videos (encoded to MPEG2, with a duration of 10 seconds) are characterized by a broad range of spatial and temporal information, which allows drawing general conclusions out of the assessment. The 8 original videos are subjected to practical transmission impairment scenarios generated by a Network Emulator (NETEM) and Video LAN [28]. Packet loss rate, jitter, delay, and throughput were the considered distortions resulting from video transmission, whereas their values were chosen based on ITU and ETSI recommendations [28,29].…”

Section: Retrieved Video Quality Databasementioning

confidence: 99%

Unsupervised deep learning for real-time assessment of video streaming services

Vega

Mocanu

Liotta

2017

Multimed Tools Appl

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Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network conditions. This means that, subjective video quality assessment studies, or complex objective video quality assessment metrics, which would be best suited from the accuracy perspective, cannot be used for this tasks (due to their high requirements in terms of time and complexity, in addition to their lack of scalability). In this paper we propose a light-weight No Reference (NR) method that, by means of unsupervised machine learning techniques and measurements on the client side is able to assess quality in real-time, accurately and in an adaptable and scalable manner. Our method makes use of the excellent density estimation capabilities of the unsupervised deep learning techniques, the restricted Boltzmann machines, and light-weight video features computed just on the impaired video to provide a delta of quality degradation. We have tested our approach in two network impaired video sets, the LIMP and the ReTRiEVED video quality databases, benchmarking the results of our method against the well-known full reference metric VQM. We have obtained levels of accuracy of at least 85% in both datasets using all possible cases.

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“…The goal of this article is to compare the performance of commonly used mapping models from newly proposed complete video quality database ReTRiEVED [12] and to recommend optimal QoS to QoE mapping model for different key QoS parameters: delay, jitter, and packet loss rate and throughput limitation.…”

Section: Introductionmentioning

confidence: 99%

QoS to QoE mapping model for wired/wireless video communication

Battisti

Carli

Paudyal

2014

2014 Euro Med Telco Conference (EMTC)

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Multimedia service providers have to deliver video content through bandwidth limited and error-prone networks with agreed level of perceived video quality to customers for specific applications. To this aim service providers must devise a strategy to monitor the perceived quality and automatically adapt it when necessary. Measuring perceived quality is challenging for service providers. So there is the need for a mapping model to predict perceived video quality from system related quality of service parameters. In this article performance comparison of widely used quality of service to quality of experience mapping models has been presented and optimal solution has been recommended for key quality of service parameters: jitter, delay, packet loss rate and throughput limitation. For this purpose the freely available video quality database ReTRiEVED has been used

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A study on the effects of quality of service parameters on perceived video quality

Cited by 26 publications

References 7 publications

Impact of video content and transmission impairments on quality of experience

Impact of video content and transmission impairments on quality of experience

Unsupervised deep learning for real-time assessment of video streaming services

QoS to QoE mapping model for wired/wireless video communication

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