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

TL;DR

This paper introduces 2BiVQA, a novel blind video quality assessment model for user-generated videos that leverages dual Bi-LSTM networks to effectively capture spatial and temporal dependencies, achieving high accuracy with lower computational cost.

Contribution

The paper proposes a new BVQA model for UGC videos using dual Bi-LSTM networks for improved spatial-temporal feature pooling, with demonstrated superior performance.

Findings

2BiVQA outperforms existing models on large-scale UGC datasets.

It achieves high accuracy with lower computational cost.

The source code is publicly available for reproducibility.

Abstract

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