  In Neural Processing Letters 50,3 (2019) a machine learning approach to blind
video quality assessment was proposed. It is based on temporal pooling of
features of video frames, taken from the last pooling layer of deep
convolutional neural networks. The method was validated on two established
benchmark datasets and gave results far better than the previous
state-of-the-art. In this letter we report the results from our careful
reimplementations. The performance results, claimed in the paper, cannot be
reached, and are even below the state-of-the-art by a large margin. We show
that the originally reported wrong performance results are a consequence of two
cases of data leakage. Information from outside the training dataset was used
in the fine-tuning stage and in the model evaluation.
