Predicting the Empirical Distribution of Video Quality Scores Using Recurrent Neural Networks

Abstract

Video quality assessment is a crucial routine in the broadcasting industry. Due to the duration and the excessive number of video files, a computer-based video quality assessment mechanism is the only solution. While it is common to measure the quality of a video file at the compression stage by comparing it against the raw data, at later stages, no reference video is available for comparison. Therefore, a noreference (Blind) video quality assessment (NR-VQA) technique is essential. The current NR-VQA methods predict only the mean opinion score (MOS) and do not provide further information about the distribution of people score. However, this distribution is informative for the evaluation of QoE. In this paper, we propose a method for predicting the empirical distribution of human opinion scores in the assessment of video quality. To the best of our knowledge, this is the first paper to investigate prediction of the distribution of human opinion scores for video quality. To this end, we extract some frame-level features, and next, we feed these features to a recurrent neural network. Finally, the distribution of opinion score is predicted in the last layer of the RNN. The experiments show that averages of predicted distributions have comparable or better results with previous methods on the KonVid-1k dataset.

Candlestick Pattern Classification Using Feedforward Neural Network

“…Fuzzy Classifier [28], Support Vector Machine [29], Recurrent Neural Networks [30], [31], or even ensemble method [32]. Another more interpretable Machine Learning method, namely the Decision Tree [33], also could be applied shortly.…”