Advances in Perceptual Quality Assessment of User Generated Contents

Dear Colleagues,

Due to the rapid development of mobile devices and wireless networks in recent years, creating, watching and sharing user-generated content (UGC) through various applications such as social media has become a popular daily activity for the general public. User-generated content in these applications exhibits markedly different characteristics than conventional, professionally generated content (PGC). Unlike professionally generated content, user-generated content is generally captured in the wild by ordinary people using diverse capture devices, and may suffer from complex real-world distortions, such as overexposure, underexposure, camera shakiness, etc., which also pose challenges for quality assessment. On one hand, an effective quality assessment (QA) model to evaluate the perceptual quality of user-generated contents can help the service provider recommend high-quality contents to users, and on the other hand can guide the development of more effective content processing algorithms.

Although subjective and objective quality assessments have been carried out in this area for many years, most of them focused on professionally generated content, without considering the specific characteristics of user-generated content. This Topic seeks original submissions and the latest technologies concerning the perceptual quality assessment of user-generated content, including—but not limited to—image/video/audio quality assessment databases/metrics for user-generated content, perceptual processing, compression, enhancement, and distribution of user-generated contents. Submissions pertaining to related practical applications and model development for user-generated content are also welcome.

Prof. Dr. Guangtao Zhai
Dr. Xiongkuo Min
Dr. Menghan Hu
Dr. Wei Zhou
Topic Editors