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Recently, with the growing popularity of mobile devices as well as videosharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-GeneratedContent (UGC) videos have become increasingly common and now account for alarge portion of multimedia traffic on the internet. Unlike professionallygenerated videos produced by filmmakers and videographers, typically, UGCvideos contain multiple authentic distortions, generally introduced duringcapture and processing by naive users. Quality prediction of UGC videos is ofparamount importance to optimize and monitor their processing in hostingplatforms, such as their coding, transcoding, and streaming. However, blindquality prediction of UGC is quite challenging because the degradations of UGCvideos are unknown and very diverse, in addition to the unavailability ofpristine reference. Therefore, in this paper, we propose an accurate andefficient Blind Video Quality Assessment (BVQA) model for UGC videos, which wename 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consistsof three main blocks, including a pre-trained Convolutional Neural Network(CNN) to extract discriminative features from image patches, which are then fedinto 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 imagepatches, while the second allows capturing longrange dependencies betweenframes to account for the temporal memory effect. Experimental results onrecent large-scale UGC VQA datasets show that 2BiVQA achieves high performanceat lower computational cost than most state-of-the-art VQA models. The sourcecode of our 2BiVQA metric is made publicly available at:https://github.com/atelili/2BiVQA

2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos