PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content

Recent years have witnessed a surge of professional user-generated content (PUGC) based video services, coinciding with the accelerated proliferation of video acquisition devices such as mobile phones, wearable cameras, and unmanned aerial vehicles. Different from traditional UGC videos by impromptu shooting, PUGC videos produced by professional users tend to be carefully designed and edited, receiving high popularity with a relatively satisfactory playing count. In this paper, we systematically conduct the comprehensive study on the perceptual quality of PUGC videos and introduce a database consisting of 10,000 PUGC videos with subjective ratings. In particular, during the subjective testing, we collect the human opinions based upon not only the MOS, but also the attributes that could potentially influence the visual quality including face, noise, blur, brightness, and color. We make the attempt to analyze the large-scale PUGC database with a series of video quality assessment (VQA) algorithms and a dedicated baseline model based on pretrained deep neural network is further presented. The cross-dataset experiments reveal a large domain gap between the PUGC and the traditional user-generated videos, which are critical in learning based VQA. These results shed light on developing next-generation PUGC quality assessment algorithms with desired properties including promising generalization capability, high accuracy, and effectiveness in perceptual optimization. The dataset and the codes are released at https://github.com/wlkdb/pugcq_create.