Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

ST-RRED scores? #28

Closed
Susmit-A opened this issue Mar 14, 2021 · 1 comment
Closed

ST-RRED scores? #28

Susmit-A opened this issue Mar 14, 2021 · 1 comment

Comments

@Susmit-A
Copy link

Susmit-A commented Mar 14, 2021

How do you obtain the ST-RRED scores?

The scikit-video implementation returns 3 values - (strred_array, strred, strredssn). On DAVIS, with sigma=10, all three values for denoised videos have a mean less than 0.1.
The PSNR for the same set of videos is 38.94.

The sum over strred_array on each video gives an average of 5.0790 over all videos.

@m-tassano
Copy link
Owner

Extracts of the script which I use to compute all the scores can be found below. Note that I read the sequences with skvideo.io.vread as grayscale sequences. I basically use pandas to compute the means for all scores for each value of sigma and for each algorithm.

from skvideo.io import vread
from skvideo.measure import viideo_score, strred

...
# open the reference sequence
refseq = vread(seq_ref_path, num_frames=nframes, outputdict={"-pix_fmt": "gray"})

# iterate over the list of sigmas and algorithms
  for sigma, algo in product(sigmaL, algoD.keys()):
    denseq = vread(seq_den_path, num_frames=nframes, outputdict={"-pix_fmt": "gray"})
    _, strred_fr, strred_rr = strred(refseq, denseq)
...
# save all the scores as csv

Later, I use pandas to open csv and compute the mean for each metric. The results are the values which appear in the paper.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants