No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and
important problem for social and streaming media applications. Efficient and accurate video …

Smartphones and other consumer devices capable of capturing video content and sharing it
on social media in nearly real time are widely available at a reasonable cost. Thus, there is a …

Satisfying the requirements of devices and users of online video streaming services is a
challenging task. It requires not only managing the network quality of service but also to …

The great variations of videographic skills in videography, camera designs, compression
and processing protocols, communication and bandwidth environments, and displays leads …

It is an important task to design models for universal no-reference video quality assessment
(NR-VQA) in multiple video processing and computer vision applications. However, most …

Due to the wide range of different natural temporal and spatial distortions appearing in user
generated video content, blind assessment of natural video quality is a challenging research …

Blind video quality assessment (BVQA) is of great importance for video-related applications,
yet still challenging even in this deep learning era. The difficulty lies in the shortage of large …

Video quality assessment (VQA) methods focus on particular degradation types, usually
artificially induced on a small set of reference videos. Hence, most traditional VQA methods …

Video quality assessment (VQA) plays a vital role in the field of video processing, including
areas of video acquisition, video filtering in retrieval, video compression, video restoration …

Distortions from spatial and temporal domains have been identified as the dominant factors
that govern the visual quality. Though both have been studied independently in deep …