  Recently, many video enhancement methods have been proposed to improve video
quality from different aspects such as color, brightness, contrast, and
stability. Therefore, how to evaluate the quality of the enhanced video in a
way consistent with human visual perception is an important research topic.
However, most video quality assessment methods mainly calculate video quality
by estimating the distortion degrees of videos from an overall perspective. Few
researchers have specifically proposed a video quality assessment method for
video enhancement, and there is also no comprehensive video quality assessment
dataset available in public. Therefore, we construct a Video quality assessment
dataset for Perceptual Video Enhancement (VDPVE) in this paper. The VDPVE has
1211 videos with different enhancements, which can be divided into three
sub-datasets: the first sub-dataset has 600 videos with color, brightness, and
contrast enhancements; the second sub-dataset has 310 videos with deblurring;
and the third sub-dataset has 301 deshaked videos. We invited 21 subjects (20
valid subjects) to rate all enhanced videos in the VDPVE. After normalizing and
averaging the subjective opinion scores, the mean opinion score of each video
can be obtained. Furthermore, we split the VDPVE into a training set, a
validation set, and a test set, and verify the performance of several
state-of-the-art video quality assessment methods on the test set of the VDPVE.
