  In this paper, we present a simple but effective method to enhance blind
video quality assessment (BVQA) models for social media videos. Motivated by
previous researches that leverage pre-trained features extracted from various
computer vision models as the feature representation for BVQA, we further
explore rich quality-aware features from pre-trained blind image quality
assessment (BIQA) and BVQA models as auxiliary features to help the BVQA model
to handle complex distortions and diverse content of social media videos.
Specifically, we use SimpleVQA, a BVQA model that consists of a trainable Swin
Transformer-B and a fixed SlowFast, as our base model. The Swin Transformer-B
and SlowFast components are responsible for extracting spatial and motion
features, respectively. Then, we extract three kinds of features from Q-Align,
LIQE, and FAST-VQA to capture frame-level quality-aware features, frame-level
quality-aware along with scene-specific features, and spatiotemporal
quality-aware features, respectively. Through concatenating these features, we
employ a multi-layer perceptron (MLP) network to regress them into quality
scores. Experimental results demonstrate that the proposed model achieves the
best performance on three public social media VQA datasets. Moreover, the
proposed model won first place in the CVPR NTIRE 2024 Short-form UGC Video
Quality Assessment Challenge. The code is available at
\url{https://github.com/sunwei925/RQ-VQA.git}.
