Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset

Tiotsop, Lohic Fotio; Servetti, Antonio; Masala, Enrico

doi:10.2352/issn.2470-1173.2020.11.hvei-093

Cited by 1 publication

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“…For the experiments, we considered five datasets, namely, the VQEG-HD1, the VQEG-HD3, the VQEG-HD5 [28], the Netflix Public [8] and the ITS4S [29] datasets. Each VQEG dataset comprises ratings from 24 subjects for approximately 160 stimuli.…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…In this section, we used our proposed RMLE approach and SUREAL software to estimate the quality of subjectively annotated datasets without adding synthetic noise, as described in Section VI-B. For the experiment, we considered five different datasets, i.e., the four datasets used in Section VI-B plus the ITS4S dataset [29]. Even without the addition of synthetic noise, the ratings in these datasets exhibit some level of noise due to inherent subject inconsistency.…”

Section: E Assessing the Uncertainty On The Estimated Subjective Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Modeling Subject Scoring Behaviors in Subjective Experiments Based on a Discrete Quality Scale

Tiotsop,

Servetti,

Barkowsky

et al. 2024

IEEE Trans. Multimedia

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Several approaches have been proposed to estimate quality in subjective experiments while highlighting peculiar subject behaviors. However, there is some room for improvement in existing approaches, both in terms of robustness to noise and the ability to accurately indicate several peculiar subject behaviors in subjective experiments. This work advances the state-of-the-art in three main directions: i) A new approach to estimate the subjective quality from noisy ratings is proposed and is shown to be more robust to noise than are four state-ofthe-art approaches; ii) a novel subject scoring model is proposed that makes it possible to highlight several peculiar behaviors typically observed in subjective experiments; and iii) our proposed probabilistic subject scoring model results from the proof of a theorem, whereas in previous approaches a probabilistic scoring model is assumed a priori. This represents an important first step toward models supported by a stronger theoretical foundation. Numerical experiments conducted on several datasets highlight the effectiveness of our proposal.

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Section: A Experimental Settingsmentioning

confidence: 99%

Section: E Assessing the Uncertainty On The Estimated Subjective Qualitymentioning

confidence: 99%

Modeling Subject Scoring Behaviors in Subjective Experiments Based on a Discrete Quality Scale

Tiotsop,

Servetti,

Barkowsky

et al. 2024

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2020: Human Vision and Electronic Imaging proceedings.

Cited by 1 publication

References 0 publications

Modeling Subject Scoring Behaviors in Subjective Experiments Based on a Discrete Quality Scale

Modeling Subject Scoring Behaviors in Subjective Experiments Based on a Discrete Quality Scale

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