Current deep video quality assessment (VQA) methods are usually with high computational
costs when evaluating high-resolution videos. This cost hinders them from learning better …

The rapid increase in user-generated-content (UGC) videos calls for the development of
effective video quality assessment (VQA) algorithms. However, the objective of the UGC …

The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment
(VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on …

The increased resolution of real-world videos presents a dilemma between efficiency and
accuracy for deep Video Quality Assessment (VQA). On the one hand, kee** the original …

Video quality assessment (VQA) aims to simulate the human perception of video quality,
which is influenced by factors ranging from low-level color and texture details to high-level …

Compared with spatial counterparts, temporal relationships between frames and their
influences on video quality assessment (VQA) are still relatively under-studied in existing …

Video quality assessment especially for a massive scale of user-generated content is an
essential yet challenging computer vision and video analysis problem. Prior methods have …

In this work, we propose a no-reference video quality assessment method, aiming to achieve
high-generalization capability in cross-content,-resolution and-frame rate quality prediction …

Recent learning-based video quality assessment (VQA) algorithms are expensive to
implement due to the cost of data collection of human quality opinions, and are less robust …

We propose a new model for no-reference video quality assessment (VQA). Our approach
uses a new idea of highly-localized space-time (ST) slices called Space-Time Chips (ST …