Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/banterle/nor-vdpnetpp

https://github.com/banterle/nor-vdpnetpp

Science Score: 39.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: banterle
  • License: bsd-3-clause-clear
  • Language: Python
  • Default Branch: main
  • Size: 23.3 MB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Created about 4 years ago · Last pushed 11 months ago
Metadata Files
Readme License

README.md

NoR-VDPNet++

NoR-VDPNet++ is a deep-learning based no-reference metric trained on HDR-VDP. Traditionally, HDR-VDP requires a reference image, which is not possible to have in some scenarios.

HDR-VDP

NoR-VDPNet++ is a no-reference metric, so it requires a single image in order to asses its quality. NoR-VDPNet can be trained on High Dynamic Range (HDR) images or Standard Dynamic Range (SDR) images (i.e., classic 8-bit images).

NoR-VDPNet++

DEPENDENCIES:

Requires the PyTorch library along with Image, NumPy, SciPy, Matplotlib, glob2, pandas, and scikit-learn.

As the first step, you need to follow the instructions for installing PyTorch.

To install dependencies, please use the following command:

bash pip3 install numpy, scipy, matplotlib, glob2, pandas, image, scikit-learn, opencv-python.

HOW TO RUN IT:

To run our metric on a folder of images (i.e., JPEG, PNG, EXR, HDR, and MAT files), you need to launch the file norvdpnet.py. Some examples:

Testing SDR images for the trained distortions (see the paper):

python3 norvdpnetpp.py SDR /home/user00/images_to_be_sdr/

Testing HDR images after JPEG-XT compression:

python3 norvdpnetpp.py HDR_COMP /home/user00/images_to_be_hdr/

Testing HDR images after tone mapping operators:

python3 norvdpnetpp.py SDR_TMO /home/user00/images_to_be_sdr/

Testing images after inverse tone mapping operators:

python3 norvdpnetpp.py HDR_ITMO /home/user00/images_to_be_hdr/

WEIGHTS DOWNLOAD:

Weights can be downloaded here: SDR, HDRC, TMO, and ITMO.

Note that these weights are meant to model ONLY determined distortions; please see reference to have a complete overview.

DO NOT:

There are many people use NoR-VDPNet++ in an appropriate way:

1) Please do not use weightsnorsdr for HDR images;

2) Please do not use weightsnorjpg_xt for SDR images;

3) Please do not use weightsnortmo for HDR images; only gamma-encoded SDR images!!!

4) Please do not use weightsnoritmo for SDR images;

5) Please do not use weights for different distortions.

DATASET PREPARATION:

Coming soon.

TRAINING:

Coming soon.

REFERENCE:

If you use NoR-VDPNet in your work, please cite it using this reference:

@ARTICLE{10089442, author={Banterle, Francesco and Artusi, Alessandro and Moreo, Alejandro and Carrara, Fabio and Cignoni, Paolo}, journal={IEEE Access}, title={NoR-VDPNet++: Real-Time No-Reference Image Quality Metrics}, year={2023}, volume={11}, number={}, pages={34544-34553}, doi={10.1109/ACCESS.2023.3263496} }

Owner

  • Name: Francesco Banterle
  • Login: banterle
  • Kind: user
  • Location: Italy
  • Company: ISTI-CNR

GitHub Events

Total
  • Push event: 22
Last Year
  • Push event: 22

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 34
  • Total Committers: 1
  • Avg Commits per committer: 34.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 23
  • Committers: 1
  • Avg Commits per committer: 23.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Francesco Banterle f****e@g****m 34

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: 30 days
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 2.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • hanjinrong (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels