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In Neural Processing Letters 50,3 (2019) a machine learning approach to blindvideo quality assessment was proposed. It is based on temporal pooling offeatures of video frames, taken from the last pooling layer of deepconvolutional neural networks. The method was validated on two establishedbenchmark datasets and gave results far better than the previousstate-of-the-art. In this letter we report the results from our carefulreimplementations. The performance results, claimed in the paper, cannot bereached, and are even below the state-of-the-art by a large margin. We showthat the originally reported wrong performance results are a consequence of twocases of data leakage. Information from outside the training dataset was usedin the fine-tuning stage and in the model evaluation.

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