Video quality evaluation in the cloud

Keimel, Christian; Habigt, Julian; Horch, Clemens; Diepold, Klaus

doi:10.1109/pv.2012.6229729

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…First studies by Keimel et al [140,141] have shown that crowdtesting can indeed provide results similar to the results from formal subjective testing in a laboratory environment. But as also discussed by Keimel et al [138], many challenges still remain before crowdtesting is a universally acceptable replacement for formal subjective testing.…”

Section: Roommentioning

confidence: 86%

Design of Video Quality Metrics with Multi-Way Data Analysis

Keimel¹

2016

T-Labs Series in Telecommunication Services

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Video quality metrics determine the visual quality of distorted video sequences by using prediction models based on objectively measurable features and are therefore an alternative to the time-consuming and costly subjective video quality assessment. In the conventional design approach to video quality metrics, however, the temporal nature of video is often considered only inadequately due to the use of temporal pooling in the prediction process. Moreover, this approach also often requires knowledge about the human visual system that is not readily or only partly available. In this thesis, I therefore propose a data driven design methodology using multi-way data analysis for the design of video quality metrics. This data driven design approach not only requires no detailed knowledge of the human visual system, but also allows for a proper consideration of the temporal nature of video by using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, I demonstrate that this purely data driven approach not only outperforms video quality metrics in the state-of-the-art that are often highly optimised towards specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling.iii

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Section: Roommentioning

confidence: 86%

Design of Video Quality Metrics with Multi-Way Data Analysis

Keimel¹

2016

T-Labs Series in Telecommunication Services

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“…The same trend is emerging in the cloud services as well [11,12]. Therefore, in this section it is tried to present justifications on cloud-crowd computing by answering the following questions:…”

Section: Injecting Crowd Into Cloud Servicesmentioning

confidence: 99%

“…An example is the crowdsourcing system reported in [12] that considers a crowdsourcing system with the mission of video quality testing and then tries shifting the videos onto the cloud, thus optimizing the production of videos to the workers. Lenk et al based on such works, redesigned the cloud computing service stack and offered a new crowdsourcing layer on top of it entitled human as a service [46].…”

Section: Where To Inject Crowd?mentioning

confidence: 99%

“…The work done in [12] considers a crowdsourcing system with the mission of video quality testing and then tries shifting the videos onto the cloud, thus optimizing the production of videos to the workers. So providing such crowdsourcing systems as services in SaaS layer can derive a benefit from cloud storage, which is the aim of that work.…”

Section: Crowd-enhanced Saasmentioning

confidence: 99%

“…Nowadays, crowds are increasingly employed in software and used alongside with the computers to solve problems. The same trend is emerging in the cloud services as well [11,12]. Crowd-enhanced cloud which is the goal of this paper is a type of distributed system created as a result of injecting crowdsourcing into cloud computing at the lowest level of cloud hierarchy a.k.a resource pool.…”

Section: Introductionmentioning

confidence: 98%

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Crowd-Enhanced Cloud Services: Issues and Directions

Arbabi¹,

Bakhsh²,

Sharifi³

2015

IJCA

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Cloud computing systems have emerged as a type of distributed systems in which a multitude of interconnected machines are gathered and recruited over the internet to help solve a computation or data-intensive problem. There are large numbers of cases in which Cloud techniques solely are not able to solve the job due to the nature of the tasks. To overcome this problem recently a strong inclination has emerged towards enlisting the human intelligence and wisdom of crowds a.k.a. Crowdsourcing in combination with the machine automated techniques. In this paper the authors propose a model for integrating crowds of people in the Cloud environments to enrich Cloud computing environments to be able to provide hybrid human-machine services enabling it to solve a wider variety of problems which some of them are studied here. The authors nickname these rich types of services, Crowd-enhanced Cloud services. At the end, the modality and challenges of this convergence and its future trends are explored.

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Crowdsourcing in QoE Evaluation

Hoßfeld

Keimel²

2014

Quality of Experience

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Video quality evaluation in the cloud

Cited by 17 publications

References 12 publications

Design of Video Quality Metrics with Multi-Way Data Analysis

Design of Video Quality Metrics with Multi-Way Data Analysis

Crowd-Enhanced Cloud Services: Issues and Directions

Crowdsourcing in QoE Evaluation

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