Towards Explainable Video Quality Assessment

Official Repository for ACMMM 2023 Paper: "Towards Explainable Video Quality Assessment: a Database and a Language-prompt Approach." Paper Link: Arxiv

Dataset Link: Google Drive, Baidu Pan.

Welcome to visit Sibling Repositories from our team:

FAST-VQA

DOVER

Zero-shot BVQI

The database (Maxwell, training part) has been released.

The code, demo and pre-trained weights of MaxVQA are released in this repo.

Installation

Install and modify OpenCLIP:

git clone https://github.com/mlfoundations/open_clip.git
cd open_clip
sed -i '92s/return x\[0\]/return x/' src/open_clip/modified_resnet.py 
pip install -e .

Install DOVER for Pre-processing and FAST-VQA weights:

git clone https://github.com/vqassessment/DOVER.git
cd DOVER
pip install -e .
mkdir pretrained_weights 
cd pretrained_weights 
wget https://github.com/VQAssessment/DOVER/releases/download/v0.1.0/DOVER.pth 

MaxVQA

Gradio Demo

demo_maxvqa.py

You can maintain a custom service for multi-dimensional VQA.

Inference from Videos

infer_from_videos.py

Inference from Pre-extracted Features

infer_from_feats.py

For the first run, the script will extract features from videos.

Training on Mixed Existing VQA Databases

For the default setting, train on LIVE-VQC, KoNViD-1k, and YouTube-UGC.

train_multi_existing.py -o LKY.yml

You can also modify the yaml file to include more datasets for training.

Obtaining Data for the Maxwell Database

Will be added soon.

Citation

Please feel free to cite our paper:

@article{wu2023maxvqa,
  title={Towards Explainable In-the-Wild Video Quality Assessment: a Database and a Language-Prompted Approach},
  author={Wu, Haoning and Zhang, Erli and Liao, Liang and Chen, Chaofeng and Hou, Jingwen and Wang, Annan  and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={Arxiv Preprint},
  year={2023}
}