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README: Link does https://qualinet.github.io/databases/ not work.

https://qualinet.github.io/databases/video/epfl_polimi_video_quality_assessment_database/

Hello, I downloaded the *.zip file unfortunately, it asks for a password to unzip it. I cannot find it on the webpage since it is indicated: ZIP archive, no password Link: http://vqa.como.polimi.it/QAdatabase/online_DB.zip

Thanks for your help!

If you want to add a new database, please edit this template and fill out the following info:

• title: CVQAD (Compressed Video Quality Assessment Dataset)
• categories: Video
• excerpt: 1,022 compressed videos (including UGC), 32 encoders of 5 compression standards (H.265/HEVC, H.266/VVC, AV1, H.264/AVC, and VP9), 3 target bitrates (1,000 kbps, 2,000 kbps, and 4,000 kbps).
• author: MSU Graphics and Media Lab / A. Antsiferova, S. Lavrushkin, M. Smirnov, A. Gushchin, D. Vatolin, and D. Kulikov
• partner: –
• external_link: https://videoprocessing.ai/datasets/vqa.html
• doi: –
• access: Fill out the form on the CVQAD page, and the dataset will be sent to you within a few hours.
• publicly_available: true
• citation: If you use this database in your research, we kindly ask that you follow the copyright notice and cite the following paper (you can also find the Bibtex citation template on the paper page)
• references:
  • Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov, Aleksandr Gushchin, Dmitriy Vatolin, and Dmitriy Kulikov, “Video compression dataset and benchmark of learning-based video-quality metrics,” in Thirty-sixth Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track, 2022.
• license: CCBY
• contact_name: Maksim Smirnov
• contact_email: [email protected]
• tags: VQA, IQA, Video quality assessment, Image quality assessment, Compressed quality
• subjective_scores: true
• total: 1,022
• src: –
• hrc: –
• ratings: 320K
• resolution: Full HD (1080p )
• method: Subjectify.us (Bradley-Terry model)

If you want, add a longer description of the database here:

The CVQA dataset focuses on the compressed video quality assessment problem. It contains 1,022 distorted streams, produced from 36 reference videos, which were originally collected using space-time-complexity clustering from a pool of more that 18,000 videos with the average bitrate of 130 Mbps. Coding artifacts were obtained by compressing videos through several encoders (32 in total) of different compression standards: H.265/HEVC, H.266/VVC, AV1, H.264/AVC, and VP9. There were two different presets (30 FPS and 1 FPS) and three target bitrates — 1,000 kbps, 2,000 kbps, and 4,000 kbps. More than 320,000 subjective scores were collected through the Subjectify.us crowdsourcing platform, which employs a Bradley-Terry model to transform the results of pairwise voting into a score for each video. Content includes nature, sport, humans close up, gameplays, music videos, water stream or steam, CGI, and user-generated content. Several effects and distortions are also presented: shaking, slow-motion, grain / noisy, too dark / bright regions, macro shooting, captions (text), and extraneous objects.

Note: You can use Markdown formatting to highlight text, include external images, create tables, etc. Use the "Preview" function to preview your post.*

If you want to add a new database, please edit this post and fill out the following info. Please keep the yaml marker intact and only replace the values.

- title: The title of the database
- database: A longer name for the database (can be the same as title)
- categories: Any of `Image`, `Video`, `Audiovisual`, `Audio`
- excerpt: A short description (optional)
- author: The institution/author
- partner: 'true' if you are a QUALINET partner (optional), otherwise false
- external_link: A link to the database
- doi: A Digital Object Identifier (DOI) (optional)
- access: A description of how to access the database (e.g. tell people to follow the link, fill out a form, etc.)
- publicly_available: 'true' if the database is publicly accessible
- citation: A short description of how to cite the database (optional)
- references: A list of references (optional), e.g.
    JD20a: John Doe, A sample reference, 2020`
    JD20b: John Doe, Another sample reference, 2020`
- license: The name of the license, or any custom license statements (optional but recommended)
- contact_name: A name or description of how to contact the author(s)
- contact_email: A contact email (this will be public)
- tags:
    - tag1
    - tag2
    - tag3 (these are all optional)
- subjective_scores: 'true' if the database has subjective scores (optional)
- total: Total number of sequences in the database (optional)
- src: The number of SRCs (optional)
- hrc: The number of HRCs (optional)
- ratings: Number of subjective ratings (optional)
- resolution: Resolutions used (optional)
- method: Subjective test method used (optional)

If you want, add a longer description of the database here:

(add a longer description here)

Note: You can use Markdown formatting to highlight text, include external images, create tables, etc. Use the "Preview" function to preview your post.*

If you want to add a new database, please edit this template and fill out the following info:

• title: Real Haze Video Database
• categories: Video
• excerpt: A short description (optional)
• author: Shenzhen University/Ying Chu, Guoxing Luo, Fan Chen
• partner: true if you are a QUALINET partner (optional)
• external_link: https://drive.google.com/file/d/1zY0LwJ-yNB8u1JTAJU2X7ZkiYXsBX7BF/view?usp=sharing
• doi: A Digital Object Identifier (DOI) (optional)
• access: The original raw haze videos are downloaded from public websites with free licences. Researcher shall use the Database only for non-commercial research and educational purposes.
• publicly_available: true
• citation: The database is to be cited in the bibliography as:
  Y. Chu, G. Luo, and F. Chen, "A Real Haze Video Database for Haze Level Evaluation," Proc. of the Thirteenth International Conference on Quality of Multimedia Experience, Jun 2021.
• references: A list of references (optional), e.g.
  • [JD20a] John Doe, A sample reference, 2020
  • [JD20b] John Doe, Another sample reference, 2020
• license: [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the RHVD database (the "Database") at Shenzhen University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
  The original raw haze videos are downloaded from public websites with free licences. Researcher shall use the Database only for non-commercial research and educational purposes.
  Shenzhen University makes no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
  Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the RHVD team, Shenzhen University, including its employees, trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
  Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
  Shenzhen University reserve the right to terminate Researcher's access to the Database at any time.
  If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
• contact_name: Ying Chu
• contact_email: [email protected]
• tags: Any tags you wish to add (optional)
• subjective_scores: true
• total: 403
• src: The number of SRCs (optional)
• hrc: The number of HRCs (optional)
• ratings: 40
• resolution: 720p, 1080p
• method: SS method

Current video quality assessment methods mainly deal with videos with distortions generated in compression or capture process, few researchers focus on perceptual degradation caused by heavy haze in real outdoor scenes. However, assessment of haze level in such application scenario is important. For instance, in the advanced driver assistance system the accurate evaluation of haze level helps to properly release warning signals to drivers, which may avoid latent traffic accidents and save people’s lives. It is necessary to develop studies on haze video quality assessment for real outdoor scenes, and to build benchmark dataset for haze videos. To fill the gap of this benchmark lack, a real haze video database called RHVD was designed and constructed. The dataset contains 403 haze videos captured in real outdoor scenes, which were collected from the Flickr video sharing platform and post-edited in our lab. We conducted subjective quality assessment experiments on the RHVD dataset. Forty non-experts were recruited and asked to rate the haze levels for each of the video in the dataset. The original ratings were screened and normalized to calculate the final subjective scores. The RHVD dataset is free to download from the following website: https://drive.google.com/file/d/1zY0LwJ-yNB8u1JTAJU2X7ZkiYXsBX7BF/view?usp=sharing, or https://pan.baidu.com/s/11p78zwMXmUHMGDj8WDanPA with password RHVD.

These links are broken and need to be removed or replaced with correct ones:

Check via GNU parallel:

grep -hriE '(external_link:)' _posts/*.md | cut -d ":" -f2- | parallel --trim l --timeout 5 "curl --insecure -L -o /dev/null --silent --head --write-out '%{url_effective} %{http_code}\n' {}" 2>/dev/null | grep -v 200