FloLPIPS: A bespoke video quality metric for frame interpoation

Duolikun Danier, Fan Zhang, David Bull

Project | arXiv

Dependencies

The following packages were used to evaluate the model.

• python==3.8.8
• pytorch==1.7.1
• torchvision==0.8.2
• cudatoolkit==10.1.243
• opencv-python==4.5.1.48
• numpy==1.19.2
• pillow==8.1.2
• cupy==9.0.0

Usage

Video-based Evaluation

from flolpips import calc_flolpips
ref_video = '<path to the reference>.mp4'
dis_video = '<path to the distorted>.mp4'
res = calc_flolpips(dis_video, ref_video)

Triplet Frame-based Evalation

from flolpips import Flolpips
import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
eval_metric = Flolpips().to(device)

batch = 8
I0 = torch.rand(8, 3, 256, 448).to(device) # first frame of the triplet
I1 = torch.rand(8, 3, 256, 448).to(device) # third frame of the triplet
frame_dis = torch.rand(8, 3, 256, 448).to(device) # prediction of the intermediate frame
frame_ref = torch.rand(8, 3, 256, 448).to(device) # ground-truth of the intermediate frame

flolpips = eval_metric.forward(I0, I1, frame_dis, frame_ref)

Citation

@article{danier2022flolpips,
  title={FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation},
  author={Danier, Duolikun and Zhang, Fan and Bull, David},
  journal={arXiv preprint arXiv:2207.08119},
  year={2022}
}

Acknowledgement

Lots of code in this repository are adapted/taken from the following repositories:

• LPIPS
• pytorch-pwc

We would like to thank the authors for sharing their code.