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zwx8981 / TCSVT-2022-BVQA

Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
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TCSVT-2022-BVQA

Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception

Description

Source code for the following paper:

Usage

Install Requirements

python 3.8.8
pytorch 1.8.1
pytorch-ignite 0.4.1
torchsort 0.1.3
torchvision 0.9.1
Matlab R2020a
Successfully tested on Ubuntu18.04.
(Our machine is equipped with Intel Xeon(R) Gold 5220R CPU×2, and NVIDIA Quadro RTX6000 24G GPU×2.)

Download VQA Databases

Download the KoNViD-1k, CVD2014, LIVE-Qualcomm, LIVE-VQC, YouTube-UGC, and LSVQ datasets. Then, run the following ln commands in the root of this project.

ln -s KoNViD-1k_path KoNViD-1k # KoNViD-1k_path is your path to the KoNViD-1k dataset
ln -s CVD2014_path CVD2014 # CVD2014_path is your path to the CVD2014 dataset
ln -s LIVE-Qualcomm_path LIVE-Qualcomm # LIVE-Qualcomm_path is your path to the LIVE-Qualcomm dataset
ln -s LIVE-VQC_path LIVE-VQC # LIVE-VQC_path is your path to the LIVE-VQC dataset
ln -s YouTube-UGC_path YouTube-UGC # YouTube-UGC_path is your path to the YouTube-UGC dataset
ln -s LSVQ_path LSVQ # LSVQ_path is your path to the LSVQ dataset

Spatial Fearure: Transfer Knowledge from Quality-aware Pre-training

Sampling image pairs from multiple IQA databases

data_all_4inthewild.m

Combining the sampled pairs to form the training set

combine_train_4inthewild.m

Training on multiple IQA databases for 10 sessions

# Prepare source IQA databases
ln -s /&source_root/BID/ImageDatabase /&tartget_root/BVQA-2021/SpatialExtractor/IQA_database/BID/ImageDatabase
ln -s /&source_root/ChallengeDB_release/Images /&tartget_root/BVQA-2021/SpatialExtractor/IQA_database/ChallengeDB_release/Images
ln -s /&source_root/koniq-10k/1024x768 /&tartget_root/BVQA-2021/SpatialExtractor/IQA_database/koniq-10k/1024x768
ln -s /&source_root/SPAQ/TestImage /&tartget_root/BVQA-2021/SpatialExtractor/IQA_database/SPAQ/TestImage

# Start Training
python Main.py --train True --network basecnn --representation NOTBCNN --ranking True --fidelity True --std_modeling True --std_loss True --margin 0.025 --batch_size 128 --batch_size2 32 --image_size 384 --max_epochs 3 --lr 1e-4 --decay_interval 3 --decay_ratio 0.1 --max_epochs2 12

Feature extraction

Notice: Fisrt set the best model path you have trained in "get_spatialextractor_model.py". We provide a sample of pre-trained weights here:

Baidu Link: https://pan.baidu.com/s/1fakwlrv2pqRbMZLeRFRz9g code: wk9r

Google drive: https://drive.google.com/file/d/1AyMrcPvSb53eEBMUJiXSNCw2nYiKy0TK/view?usp=sharing

CUDA_VISIBLE_DEVICES=0 python CNNfeatures_Spatial.py --database=KoNViD-1k --frame_batch_size=64
CUDA_VISIBLE_DEVICES=1 python CNNfeatures_Spatial.py --database=CVD2014 --frame_batch_size=64
CUDA_VISIBLE_DEVICES=0 python CNNfeatures_Spatial.py --database=LIVE-Qualcomm --frame_batch_size=8
CUDA_VISIBLE_DEVICES=1 python CNNfeatures_Spatial.py --database=LIVE-VQC --frame_batch_size=8
CUDA_VISIBLE_DEVICES=0 python CNNfeatures_Spatial.py --database=YouTube-UGC --frame_batch_size=8
CUDA_VISIBLE_DEVICES=1 python CNNfeatures_Spatial.py --database=LSVQ --frame_batch_size=8

Motion Fearure: Transfer Knowledge from Motion Perception

Prepare the pre-trained SlowFast model file in the directory "./MotionExtractor/checkpoints/Kinetics/"

SlowFast_Model

Feature extraction

CUDA_VISIBLE_DEVICES=&gpu_id python CNNfeatures_Motion.py --database=&database --frame_batch_size=64

Final Fearure: Feature Fusion of Spatial and Motion Features

CUDA_VISIBLE_DEVICES=&gpu_id python CNNfeatures_Fusion.py --database=&database --frame_batch_size=64

Training and Evaluating on VQA Databases

# Training, under individual-dataset setting, for example 
python main.py --trained_datasets C --tested_datasets C
# Training, under mixed-database setting, for example
python main.py --trained_datasets K C L N --tested_datasets K C L N

Analyse results

# Analysis, under individual-dataset setting, for example 
python result_analysis.py --trained_datasets C --tested_datasets C
# Analysis, under mixed-database setting, for example
python result_analysis.py --trained_datasets K C L N --tested_datasets K C L N

Test Demo

The model weights provided in models/model_XXX are the saved weights when best performing in training.

# Testing, under individual-dataset setting, for example 
python test_demo.py --trained_datasets C --model_path models/model_C --video_path=data/test.mp4
# Testing, under mixed-database setting, for example
python test_demo.py --trained_datasets K C L N --model_path models/model_KCLN --video_path=data/test.mp4

Acknowledgement

This cobebase is heavily inspired by MDTVSFA (Li et al., IJCV2021).

The model-based transfer learning for video feature extraction mainly follows the implementations of UNIQUE - IQA domain (Zhang et al., TIP2021) and SlowFast - Action Recognition domain (Feichtenhofer et al., ICCV2019).

Great appreciation for their excellent works.

Citation

Should you find this repo useful to your research, we sincerely appreciate it if you cite our paper :blush: :

@article{li2022blindly,
  title={Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception},
  author={Li, Bowen and Zhang, Weixia and Tian, Meng and Zhai, Guangtao and Wang, Xianpei},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={32},
  number={9},
  pages={5944-5958},
  year={2022}
}