  Recently, with the growing popularity of mobile devices as well as video
sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated
Content (UGC) videos have become increasingly common and now account for a
large portion of multimedia traffic on the internet. Unlike professionally
generated videos produced by filmmakers and videographers, typically, UGC
videos contain multiple authentic distortions, generally introduced during
capture and processing by naive users. Quality prediction of UGC videos is of
paramount importance to optimize and monitor their processing in hosting
platforms, such as their coding, transcoding, and streaming. However, blind
quality prediction of UGC is quite challenging because the degradations of UGC
videos are unknown and very diverse, in addition to the unavailability of
pristine reference. Therefore, in this paper, we propose an accurate and
efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we
name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists
of three main blocks, including a pre-trained Convolutional Neural Network
(CNN) to extract discriminative features from image patches, which are then fed
into two Recurrent Neural Networks (RNNs) for spatial and temporal pooling.
Specifically, we use two Bi-directional Long Short Term Memory (Bi-LSTM)
networks, the first is used to capture short-range dependencies between image
patches, while the second allows capturing longrange dependencies between
frames to account for the temporal memory effect. Experimental results on
recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance
at lower computational cost than most state-of-the-art VQA models. The source
code of our 2BiVQA metric is made publicly available at:
https://github.com/atelili/2BiVQA
