Perceptual quality assessment of video using machine learning algorithm

Mustafa, Safi; Hameed, Abdul

doi:10.1007/s11760-019-01494-5

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…The proposed model performs well in terms of all four QoE aspects, the accuracy percentage ranging from 91% to 93%. The existing machine learning model [32] based on neural network, naive Bayes, and DT achieved 84% to 88% classification rate. Another model based on semi-supervised learning method [33] with 0.84 f-score, and [31] with 0.75 f1-score and classification rate up to 74%.…”

Section: A Experiments and Evaluationmentioning

confidence: 99%

Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

et al. 2020

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Section: A Experiments and Evaluationmentioning

confidence: 99%

Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

et al. 2020

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“…Mustafa and Hameed [10] had many testing scenarios (packet loss rate, various bitrates and scenes) but worked only with low resolution for video encoding (H.264 codec). They applied a new methodology to numerous machine learning applications (neural network, naïve Bayes, or decision tree) and obtained a classification rate ranging from 0.86 to 0.88.…”

Section: Related Workmentioning

confidence: 99%

Predicting Perceptual Quality in Internet Television Based on Unsupervised Learning

et al. 2020

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Quality of service (QoS) and quality of experience (QoE) are two major concepts for the quality evaluation of video services. QoS analyzes the technical performance of a network transmission chain (e.g., utilization or packet loss rate). On the other hand, subjective evaluation (QoE) relies on the observer’s opinion, so it cannot provide output in a form of score immediately (extensive time requirements). Although several well-known methods for objective evaluation exist (trying to adopt psychological principles of the human visual system via mathematical models), each of them has its own rating scale without an existing symmetric conversion to a standardized subjective output like MOS (mean opinion score), typically represented by a five-point rating scale. This makes it difficult for network operators to recognize when they have to apply resource reservation control mechanisms. For this reason, we propose an application (classifier) that derivates the subjective end-user quality perception based on a score of objective assessment and selected parameters of each video sequence. Our model integrates the unique benefits of unsupervised learning and clustering techniques such as overfitting avoidance or small dataset requirements. In fact, most of the published papers are based on regression models or supervised clustering. In this article, we also investigate the possibility of a graphical SOM (self-organizing map) representation called a U-matrix as a feature selection method.

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“…Authors (Mustafa and Hameed, 2019) created several testing scenarios (different packet loss, 12 types of scene, various bitrates) but they used only CIF and 4CIF resolution with H.264 codec. They decided to apply new metric NoDFI on several machine learning tools (neural network, naïve bayes and decision tree) and obtained prediction accuracy ranging from 0.86 to 0.88.…”

Section: Introductionmentioning

confidence: 99%

QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study

Frnda

Ďurica

Savrasovs

et al. 2020

Transport and Telecommunication Journal

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AbstractThis paper deals with an analysis of Kohonen map usage possibility for real-time evaluation of end-user video quality perception. The Quality of Service framework (QoS) describes how the network impairments (network utilization or packet loss) influence the picture quality, but it does not reflect precisely on customer subjective perceived quality of received video stream. There are several objective video assessment metrics based on mathematical models trying to simulate human visual system but each of them has its own evaluation scale. This causes a serious problem for service providers to identify a critical point when intervention into the network behaviour is needed. On the other hand, subjective tests (Quality of Experience concept) are time-consuming and costly and of course, cannot be performed in real-time. Therefore, we proposed a mapping function able to predict subjective end-user quality perception based on the situation in a network, video stream features and results obtained from the objective video assessment method.

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Perceptual quality assessment of video using machine learning algorithm

Cited by 7 publications

References 36 publications

Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

Predicting Perceptual Quality in Internet Television Based on Unsupervised Learning

QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study

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