③[ICML2024] [IQA, IAA, VQA] All-in-one Foundation Model for visual scoring. Can efficiently fine-tune to downstream datasets.
[ICCV 2023, Official Code] for paper "Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives". Official Weights and Demos provided.
[ECCV2022, TPAMI2023] FAST-VQA, and its extended version FasterVQA.
[official] Quality Assessment of In-the-Wild Videos (ACM MM 2019)
[IEEE TIP'2021] "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content", Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
A resource list and performance benchmark for blind video quality assessment (BVQA) models on user-generated content (UGC) datasets. [IEEE TIP'2021] "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content", Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
[ACMMM Oral, 2023] "Towards Explainable In-the-wild Video Quality Assessment: A Database and a Language-Prompted Approach"
Patch-VQ: ‘Patching Up’ the Video Quality Problem
A Deep Learning based No-reference Quality Assessment Model for UGC Videos
The Most Comprehensive Survey of Video Quality Assessment to Date.
[IEEE OJSP'2021] "RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content", Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
A video quality MOS prediction model for videoconferencing calls that takes temporal distortions into account
[ICME 2023 Oral, Extended to TIP (UR)] The best zero-shot VQA approach that even outperforms several fully-supervised methods.
2BiVQA is a no-reference deep learning based video quality assessment metric.
Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos
Fast Blind Natural Video Quality (V-BLIINDS)
[NeurIPS2024 D&B Spotlight] GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
[NeurIPS'2022] "Video compression dataset and benchmark of learning-based video-quality metrics", A. Antsiferova, S. Lavrushkin, M. Smirnov, A. Gushchin, D. S. Vatolin, and D. Kulikov
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