In video quality evaluation, the perceived quality is ranked by the participants using a categorical scale of five levels. To study the category learning dependency, the participants were divided into learners and no-learners, with respect to their classification accuracy. An analysis of the performance of the human unsupervised learning from machine learning models is presented in order to study the effects of category learning in the video assessment.
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