sunwei925/CompressedVQA

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

CompressedVQA

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

Description

This is a repository for the models proposed in the paper "Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos" arxiv.

The proposed models won first place on the FR track and second place on the NR track in Challenge on Quality Assessment of Compressed UGC Videos hold on IEEE ICME 2021.

Usage

Install Requirements

opencv-python==4.5.1.48
scipy==1.5.2
torch==1.5.0
torchvision==0.6.0a0+82fd1c8

Download Compressed UGC VQA database

The compressed UGC VQA database can be downloaded from http://ugcvqa.com/.

Models

You can download the trained model via:

FR VQA model: Google Drive

NR VQA model: Google Drive

Train

FR VQA model

CUDA_VISIBLE_DEVICES=0 python -u train_FR.py \
 --database UGCCompressed \
 --model_name UGCVQA_FR_model \
 --conv_base_lr 0.0001 \
 --datainfo json_files/ugcset_dmos.json \
 --videos_dir UGCCompressedVideo/ \
 --epochs 100 \
 --train_batch_size 6 \
 --print_samples 1000 \
 --num_workers 8 \
 --ckpt_path ckpts \
 --decay_ratio 0.9 \
 --decay_interval 10 \
 --reults_path results \
 --exp_version 0

NR VQA model

CUDA_VISIBLE_DEVICES=0 python -u train_NR.py \
 --database UGCCompressed \
 --model_name UGCVQA_NR_model \
 --conv_base_lr 0.0001 \
 --datainfo json_files/ugcset_mos.json \
 --videos_dir UGCCompressedVideo/ \
 --epochs 100 \
 --train_batch_size 6 \
 --print_samples 1000 \
 --num_workers 8 \
 --ckpt_path ckpts \
 --decay_ratio 0.9 \
 --decay_interval 10 \
 --reults_path results \
 --exp_version 0

Test on the Compressed UGC VQA database

FR VQA model

CUDA_VISIBLE_DEVICES=0 python -u test_FR.py \
 --database UGCCompressed \
 --model_name UGCVQA_FR_model \
 --num_workers 16 \
 --multi_gpu False \
 --datainfo_test json_files/ugcset_dmos.json \
 --videos_dir_test UGCCompressedVideo/ \
 --trained_model ckpts/UGCVQA_FR_model.pth \

NR VQA model

CUDA_VISIBLE_DEVICES=0 python -u test_NR.py \
 --database UGCCompressed \
 --model_name UGCVQA_NR_model \
 --num_workers 16 \
 --multi_gpu False \
 --datainfo_test json_files/ugcset_mos.json \
 --videos_dir_test UGCCompressedVideo/ \
 --trained_model ckpts/UGCVQA_NR_model.pth \

Test on the demo video

FR VQA

single scale + test on gpu

CUDA_VISIBLE_DEVICES=0 python test_FR_demo.py --method_name=single-scale --ref videos/UGC0034_1280x720_30_crf_00.mp4 --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt --is_gpu

using cpu (only work for windows)

python test_FR_demo.py --method_name=single-scale --ref videos/UGC0034_1280x720_30_crf_00.mp4 --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt

using multi scale strategy

CUDA_VISIBLE_DEVICES=0 python test_FR_demo.py --method_name=multi-scale --ref videos/UGC0034_1280x720_30_crf_00.mp4 --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt --is_gpu

NR VQA

single scale

CUDA_VISIBLE_DEVICES=0 python test_NR_demo.py --method_name=single-scale --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt --is_gpu

using cpu

python test_NR_demo.py --method_name=single-scale --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt

using multi scale strategy

CUDA_VISIBLE_DEVICES=0 python test_NR_demo.py --method_name=multi-scale --dist videos/UGC0034_1280x720_30_crf_22.mp4 --output result.txt --is_gpu

Citation

If you find this code is useful for your research, please cite:

@inproceedings{sun2021deep,
  title={Deep learning based full-reference and no-reference quality assessment models for compressed ugc videos},
  author={Sun, Wei and Wang, Tao and Min, Xiongkuo and Yi, Fuwang and Zhai, Guangtao},
  booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
  pages={1--6},
  year={2021},
  organization={IEEE}
}