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Volume: 33 | Article ID: art00014
Neural network-based assessment of the impact induced in video quality assessment by the semantic labels
  DOI :  10.2352/ISSN.2470-1173.2021.9.IQSP-224  Published OnlineJanuary 2021

Subjective video quality assessment generally comes across with semantically labeled evaluation scales (e.g. Excellent, Good, Fair, Poor and Bad on a single stimulus, 5 level grading scale). While suspicions about an eventual bias these labels induce in the quality evaluation always occur, to the best of our knowledge, very few state-of-the-art studies target an objective assessment of such an impact. Our study presents a neural network solution in this respect. We designed a 5-class classifier, with 2 hidden layers, and a softmax output layer. An ADAM optimizer coupled to a Sparse Categorical Cross Entropy function is subsequently considered. The experimental results are obtained out of processing a database composed of 440 observers scoring about 7 hours of video content of 4 types (high-quality stereoscopic video content, low-quality stereoscopic video content, high-quality 2D video, and low-quality 2D video). The experimental results are discussed and confrontment to the reference given by a probability-based estimation method. They show an overall good convergence between the two types of methods while pointing out to some inner applicative differences that are discussed and explained.

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C. Hernandez, Z. De La Lande Dolce, R. Bensaied, M. Mitrea, "Neural network-based assessment of the impact induced in video quality assessment by the semantic labelsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVIII,  2021,  pp 224-1 - 224-7,

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