UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

By Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, A. Bovik. IEEE Transactions on Image Processing, 2020

350 citations recorded by the source corpus.

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC videos are unpredictable, complicated, and often commingled. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and objective VQA model design. By employing a feature selection strategy on top of efficient BVQA models, we are able to extract 60 out of 763 statistical features used in existing methods to create a new fusion-based model, which we dub the VIDeo quality EVALuator (VIDEVAL), that effectively balances the trade-off between VQA performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at considerably lower computational cost than other leading models. Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient UGC video processing, transcoding, and streaming. To promote reproducible research and public evaluation, an implementation of VIDEVAL has been made available online: https://github.com/vztu/VIDEVAL.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a0d2b46a4ee9eca6f32df1c6f6bf7b1fbe6c0e0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yu Qiao
2. Luc Van Gool
3. Limin Wang
4. Ming-Hsuan Yang
5. Dacheng Tao
6. Ziwei Liu
7. Xiaogang Wang
8. Xiaoou Tang

In Signal Processing

1. Yu Qiao
2. Weisi Lin
3. Limin Wang
4. Ziwei Liu
5. Chaofeng Chen
6. Zhifeng Li
7. Yandong Wen
8. Stefano Ermon

Citations in the corpus

• VBench: Comprehensive Benchmark Suite for Video Generative Models (2023, methodology): “Meanwhile, the Video Quality Assessment (VQA) methods [63, 94, 108–114] are primarily designed for real videos, thereby neglecting the unique challenges posed by generative models, such as artifacts in synthesized videos.”
• Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels (2023, methodology): “Similar as IQA, while traditional approaches on VQA are typically based on handcraft features, e.g. TLVQM (Korhonen, 2019), VIDEVAL (Tu et al., 2021a), and RAPIQUE (Tu et al., 2021b), recent deep-learning-based methods, such as VSFA (Li et al., 2019), BVQA (Li et al., 2022), Dis-CoVQA (Wu et al.) an”
• FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling (2022, methodology): “Classical VQA Methods Classical VQA methods [31,29,20,36,35,25] handcrafted features to evaluate video quality.”
• Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives (2022, background): “Recent Video Quality Assessment (VQA) studies have gathered enormous human quality opinions [1–5] on in-the-wild user-generated contents (UGC) and attempted to use machine algorithms [6–8] to learn and predict these opinions, known as the UGCVQA problem [9].”