contivqaexp

A software tool for designing, conducting, and analyzing continuous subjective video quality assessments experiments.

https://github.com/okarras/contivqaexp

Science Score: 57.0%

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

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 6 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.7%) to scientific vocabulary

Keywords

analyzer experiment quality video
Last synced: 6 months ago · JSON representation ·

Repository

A software tool for designing, conducting, and analyzing continuous subjective video quality assessments experiments.

Basic Info
  • Host: GitHub
  • Owner: okarras
  • License: mit
  • Language: Java
  • Default Branch: master
  • Homepage:
  • Size: 2.75 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
analyzer experiment quality video
Created about 4 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md


Logo

Continuous Video Quality Assessment Experiments
"Quality is in the Eye of the Beholder"


<!--View Demo ·--> Report Bug · Request Feature

Table of Contents
  1. About the Project
  2. Built With
  3. Getting Started
  4. Usage
  5. Publications
  6. Contributing
  7. License
  8. Contact
  9. Acknowledgments

About the Project

ContiVQAExp is a software tool for designing, conducting, and analyzing subjective video quality assessment experiments. The special feature of this tool is the possibility of a continuous collection of a subject's assessment data for generic video quality characteristics during video playback. In this way, we are able to perform analyses that provide detailed insights into the relationships between the implementation of video quality characteristics and their effects on a viewer’s quality assessment.

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Built With

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Getting Started

You need to install OpenJDK and JavaFX on your computer to run the project.

We use Visual Studio Code for developement and can recommend the following YouTube Tutorial.

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Usage

The application of ContiVQAExp is shown in the following document with screenshots and descriptions.

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Publications

If you want to cite this project, we suggest to use either the citation provided above or the following reference:

Oliver Karras, Jil Klünder, and Kurt Schneider
Tool-Supported Experiments for Continuously Collecting Data of Subjective Video Quality Assessments During Video Playback
In: Softwaretechnik-Trends, 40 (1), 2019.

The details of the implementation of ContiVQAExp are reported in the following reference:

Vignesh Arulmani Sankaranarayanan
Tool-Supported Data Collection for Experiments to Subjectively Assess Vision Videos Masters Thesis
Leibniz Universität Hannover, 2019.

ContiVQAExp was successfully used for subjective video quality assessment experiments in the following reference:

Alida Rohde
Verification of Recommendations for the Production and Use of Vision Videos Based on Subjective Video Quality Assessments
Leibniz Universität Hannover, 2020.

If you want to use and cite the vision video used in the exemplary experiments, use the following reference:

Oliver Karras
Vision Video - Interaction Process of the Purchase of a Product by a Customer in a Webshop
Zenodo, 2020.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make ContiVQAExp better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Dr. rer. nat. Oliver Karras - @OliverKarras - me@oliver-karras.de

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Acknowledgments

Vignesh Arulmani Sankaranarayanan - @Vignesh-A-S as main developer of ContiVQAExp.

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Owner

  • Name: Oliver Karras
  • Login: okarras
  • Kind: user

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  ContiVQAExp: Continuous Video Quality Assessment
  Experiments
message: Please cite this software using these metadata.
type: software
authors:
  - given-names: Oliver
    family-names: Karras
    email: me@oliver-karras.de
    orcid: 'https://orcid.org/0000-0001-5336-6899'
identifiers:
  - type: url
    value: >-
      https://fb-swt.gi.de/fileadmin/FB/SWT/Softwaretechnik-Trends/Verzeichnis/Band_40_Heft_1/Karras.pdf
    description: Corresponding publication
  - type: doi
    value: 10.15488/11963
    description: Corresponding publication
repository-code: 'https://github.com/okarras/ContiVQAExp'
keywords:
  - video
  - quality
  - experiment
  - analyzer
license: MIT

GitHub Events

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Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 43
  • Total Committers: 1
  • Avg Commits per committer: 43.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 5
  • Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Top Committers
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okarras me@o****e 43
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: about 2 years ago

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  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: less than a minute
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
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Past Year
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  • Average time to close issues: N/A
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  • Issue authors: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
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