Computing Quality-of-Experience Ranges for Video Quality Estimation

Tiotsop, Lohic Fotio; Masala, Enrico; Aldahdooh, Ahmed; Wallendael, Glenn Van; Barkowsky, Marcus

doi:10.1109/qomex.2019.8743303

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Recently, however, several papers appeared in the literature underlining the limits of the MOS as a comprehensive measure of QoE while proposing more complete approaches for an effective objective evaluation of the perceptual quality as assessed by the end users [20,21,22,23]. In [24], the authors argued that for a given objective quality score, the corresponding subjective quality should be considered a probability distribution. In other words, the MOS, being a single value, does not capture all the aspects that contribute to measuring QoE.…”

Section: Related Workmentioning

confidence: 99%

Predicting Individual Quality Ratings of Compressed Images Through Deep Cnns-Based Artificial Observers

Fotio Tiotsop,

Servetti,

Barkowsky

et al. 2022

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Predicting Individual Quality Ratings of Compressed Images Through Deep Cnns-Based Artificial Observers

Fotio Tiotsop,

Servetti,

Barkowsky

et al. 2022

SSRN Journal

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“…In fact, in a number of recent papers, relying on statistical methods, authors highlighted the inability of the MOS to fully capture all the aspects necessary to measure the perceived quality of a media. In [6], the deviation from the MOS is handled by determining ranges of quality of experience (QoE) rather than a single MOS value. The authors in [24] illustrated the fundamental advantages of using the distribution of opinion scores to assess the quality rather than the MOS, thus underlining the importance of explicitly taking into account the opinions' diversity when assessing the perceived visual quality.…”

Section: Introductionmentioning

confidence: 99%

Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach

Tiotsop

Mizdos

Uhrina

et al. 2020

Multimed Tools Appl

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Subjective experiments are considered the most reliable way to assess the perceived visual quality. However, observers’ opinions are characterized by large diversity: in fact, even the same observer is often not able to exactly repeat his first opinion when rating again a given stimulus. This makes the Mean Opinion Score (MOS) alone, in many cases, not sufficient to get accurate information about the perceived visual quality. To this aim, it is important to have a measure characterizing to what extent the observed or predicted MOS value is reliable and stable. For instance, the Standard deviation of the Opinions of the Subjects (SOS) could be considered as a measure of reliability when evaluating the quality subjectively. However, we are not aware of the existence of models or algorithms that allow to objectively predict how much diversity would be observed in subjects’ opinions in terms of SOS. In this work we observe, on the basis of a statistical analysis made on several subjective experiments, that the disagreement between the quality as measured by means of different objective video quality metrics (VQMs) can provide information on the diversity of the observers’ ratings on a given processed video sequence (PVS). In light of this observation we: i) propose and validate a model for the SOS observed in a subjective experiment; ii) design and train Neural Networks (NNs) that predict the average diversity that would be observed among the subjects’ ratings for a PVS starting from a set of VQMs values computed on such a PVS; iii) give insights into how the same NN based approach can be used to identify potential anomalies in the data collected in subjective experiments.

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Performance evaluation of DVB-t image transmission over a MIMO OWC channel at 650 nm under varying turbulence regimes

Gill

Singh

2021

Wireless Netw

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Computing Quality-of-Experience Ranges for Video Quality Estimation

Cited by 8 publications

References 4 publications

Predicting Individual Quality Ratings of Compressed Images Through Deep Cnns-Based Artificial Observers

Predicting Individual Quality Ratings of Compressed Images Through Deep Cnns-Based Artificial Observers

Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach

Performance evaluation of DVB-t image transmission over a MIMO OWC channel at 650 nm under varying turbulence regimes

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