  With the rapid growth of User-Generated Content (UGC) exchanged between users
and sharing platforms, the need for video quality assessment in the wild has
emerged. UGC is mostly acquired using consumer devices and undergoes multiple
rounds of compression or transcoding before reaching the end user. Therefore,
traditional quality metrics that require the original content as a reference
cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video
Quality Assessment (NR-VQA) model that aims to address the challenges of
evaluating the diversity of video content and the assessment of its quality
without reference videos. ReLaX-VQA uses fragments of residual frames and
optical flow, along with different expressions of spatial features of the
sampled frames, to enhance motion and spatial perception. Furthermore, the
model enhances abstraction by employing layer-stacking techniques in deep
neural network features (from Residual Networks and Vision Transformers).
Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms
existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of
0.8872. We will open source the code and trained models to facilitate further
research and applications of NR-VQA: https://github.com/xinyiW915/ReLaX-VQA.
