Crowdsourcing based subjective quality assessment of adaptive video streaming

Shahid, Muhammad; Sogaard, Jacob; Pokhrel, Jeevan; Brunnström, Kjell; Wang, Kun; Tavakoli, Samira; Gracia, Narciso

doi:10.1109/qomex.2014.6982289

Cited by 16 publications

(15 citation statements)

References 6 publications

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“…In addition, users need to be physically present in the laboratory to perform the test [46]. On the other hand, crowdsourcing experiments allow an investigator to get opinions from a vast variety of subjects; in a time-flexible, test-data size scalable, and swift manner [35]. A summary of the results of both experiments is presented in Table 3.…”

Section: Discussionmentioning

confidence: 99%

“…In [9] various improvements for implementing subjective crowdsourcing experiments are proposed, so-called momento methods that increase the reliability and execution time of the crowdsourcing campaign. This work builds on our previous work [35], where the initial results from the crowdsourcing experiment are presented.…”

Section: Related Workmentioning

confidence: 99%

“…This paper presents a subjective study on HAS through crowdsourcing as briefly reported in [35]. We employ a test stimuli representative of various adaptive video streaming scenarios that are adopted in practice by most service providers to find a good ABR strategy (e.g.…”

Section: Goals and Contributionsmentioning

confidence: 99%

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On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Sogaard

Shahid

Pokhrel

et al. 2016

Multimed Tools Appl

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Video streaming services are offered over the Internet and since the service providers do not have full control over the network conditions all the way to the end user, streaming technologies have been developed to maintain the quality of service in these varying network conditions i.e. so called adaptive video streaming. In order to cater for users' Quality of Experience (QoE) requirements, HTTP based adaptive streaming solutions of video services have become popular. However, the keys to ensure the users a good QoE with this technology is still not completely understood. User QoE feedback is therefore instrumental in improving this understanding. Controlled laboratory based perceptual quality experiments that involve a panel of human viewers are considered to be the most valid method of the assessment of QoE. Besides laboratory based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Sogaard

Shahid

Pokhrel

et al. 2016

Multimed Tools Appl

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“…25 (5) confirm that this is a very efficient tool for feature selection, while producing accurate quality estimations through the use of sparse models, at the same time. As a baseline, ridge regression [16][17][18] is used, which requires a much larger number of features, as compared to LASSO for making MOS estimations, even after a feature selection preprocessing.…”

Section: Goals Of the Articlementioning

confidence: 65%

“…Crowdsourcing-based subjective VQA is an emerging technique, where test material is transferred to the viewer's premises through the Internet and the quality scores are collected through a loosely controlled environment. 5 However, subjective VQA is rather tedious, timeconsuming, and impractical to be incorporated in many realtime applications.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Shahid

Pandremmenou

Kondi

et al. 2016

J. Electron. Imaging

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This is the published version of a paper published in Journal of Electronic Imaging (JEI).Citation for the original published paper (version of record):Shahid, M. (2016) Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models. Journal of Electronic Imaging (JEI)Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Abstract. Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

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2021

Influencing Factors in Speech Quality Assessment Using Crowdsourcing

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Crowdsourcing based subjective quality assessment of adaptive video streaming

Cited by 16 publications

References 6 publications

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

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