Subjective and objective study of sharpness enhanced UGC video quality

With the popularity of video sharing applications and video conferencing systems, there has been a growth of interest to measure and enhance the quality of videos captured and transmitted by those applications. While assessing the quality of UGC videos itself is still an open question, it is even challenging to enhance the perceptual quality of UGC videos with unknown characteristics. In this work, we study the potential to enhance the quality of UGC videos by increasing the sharpen effects. To this end, we construct a subjective dataset by conducting a massive online crowdsourcing. The dataset consists of 1200 sharpness enhanced UGC videos processed from 200 UGC source videos. During subjective test, each processed video is compared with its source to capture finegrained quality difference. We propose a statistical model to precisely measure whether the quality is enhanced or degraded. Moreover, we benchmark state-of-the-art No-Reference image or video quality metrics with the collected subjective data. It is observed that most metrics do not correlate well with subjective score. This indicates the need to develop more reliable objective metrics for UGC videos.