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

[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

Video Quality Metrics

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.

Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos

2BiVQA is a no-reference deep learning based video quality assessment metric.

Enhancing Blind Video Quality Assessment with Rich Quality-aware Features

Fast Blind Natural Video Quality (V-BLIINDS)

Official implementation for "CONVIQT: Contrastive Video Quality Estimator"

[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

[Accepted by NeurIPS2024 D&B Spotlight] GAIA: Rethinking Action Quality Assessment for AI-Generated Videos