  The study of video prediction models is believed to be a fundamental approach
to representation learning for videos. While a plethora of generative models
for predicting the future frame pixel values given the past few frames exist,
the quantitative evaluation of the predicted frames has been found to be
extremely challenging. In this context, we study the problem of quality
assessment of predicted videos. We create the Indian Institute of Science
Predicted Videos Quality Assessment (IISc PVQA) Database consisting of 300
videos, obtained by applying different prediction models on different datasets,
and accompanying human opinion scores. We collected subjective ratings of
quality from 50 human participants for these videos. Our subjective study
reveals that human observers were highly consistent in their judgments of
quality of predicted videos. We benchmark several popularly used measures for
evaluating video prediction and show that they do not adequately correlate with
these subjective scores. We introduce two new features to effectively capture
the quality of predicted videos, motion-compensated cosine similarities of deep
features of predicted frames with past frames, and deep features extracted from
rescaled frame differences. We show that our feature design leads to state of
the art quality prediction in accordance with human judgments on our IISc PVQA
Database. The database and code are publicly available on our project website:
https://nagabhushansn95.github.io/publications/2020/pvqa
