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Reproducible research framework for objective video quality measures using a large-scale database approach

(2018) SOFTWAREX. 8. p.64-68
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Abstract
This work presents a framework to facilitate reproducibility of research in video quality evaluation. Its initial version is built around the JEG-Hybrid database of HEVC coded video sequences. The framework is modular, organized in the form of pipelined activities, which range from the tools needed to generate the whole database from reference signals up to the analysis of the video quality measures already present in the database. Researchers can re-run, modify and extend any module, starting from any point in the pipeline, while always achieving perfect reproducibility of the results. The modularity of the structure allows to work on subsets of the database since for some analysis this might be too computationally intensive. To this purpose, the framework also includes a software module to compute interesting subsets, in terms of coding conditions, of the whole database. An example shows how the framework can be used to investigate how the small differences in the definition of the widespread PSNR metric can yield very different results, discussed in more details in our accompanying research paper Aldahdooh et al. (0000). This further underlines the importance of reproducibility to allow comparing different research work with high confidence. To the best of our knowledge, this framework is the first attempt to bring exact reproducibility end-to-end in the context of video quality evaluation research. (C) 2017 The Authors. Published by Elsevier B.V.
Keywords
Reproducible research, Large database analysis, Video quality

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MLA
Aldahdooh, Ahmed, et al. “Reproducible Research Framework for Objective Video Quality Measures Using a Large-Scale Database Approach.” SOFTWAREX, vol. 8, 2018, pp. 64–68, doi:10.1016/j.softx.2017.09.004.
APA
Aldahdooh, A., Masala, E., Van Wallendael, G., & Barkowsky, M. (2018). Reproducible research framework for objective video quality measures using a large-scale database approach. SOFTWAREX, 8, 64–68. https://doi.org/10.1016/j.softx.2017.09.004
Chicago author-date
Aldahdooh, Ahmed, Enrico Masala, Glenn Van Wallendael, and Marcus Barkowsky. 2018. “Reproducible Research Framework for Objective Video Quality Measures Using a Large-Scale Database Approach.” SOFTWAREX 8: 64–68. https://doi.org/10.1016/j.softx.2017.09.004.
Chicago author-date (all authors)
Aldahdooh, Ahmed, Enrico Masala, Glenn Van Wallendael, and Marcus Barkowsky. 2018. “Reproducible Research Framework for Objective Video Quality Measures Using a Large-Scale Database Approach.” SOFTWAREX 8: 64–68. doi:10.1016/j.softx.2017.09.004.
Vancouver
1.
Aldahdooh A, Masala E, Van Wallendael G, Barkowsky M. Reproducible research framework for objective video quality measures using a large-scale database approach. SOFTWAREX. 2018;8:64–8.
IEEE
[1]
A. Aldahdooh, E. Masala, G. Van Wallendael, and M. Barkowsky, “Reproducible research framework for objective video quality measures using a large-scale database approach,” SOFTWAREX, vol. 8, pp. 64–68, 2018.
@article{8581453,
  abstract     = {{This work presents a framework to facilitate reproducibility of research in video quality evaluation. Its initial version is built around the JEG-Hybrid database of HEVC coded video sequences. The framework is modular, organized in the form of pipelined activities, which range from the tools needed to generate the whole database from reference signals up to the analysis of the video quality measures already present in the database. Researchers can re-run, modify and extend any module, starting from any point in the pipeline, while always achieving perfect reproducibility of the results. The modularity of the structure allows to work on subsets of the database since for some analysis this might be too computationally intensive. To this purpose, the framework also includes a software module to compute interesting subsets, in terms of coding conditions, of the whole database. An example shows how the framework can be used to investigate how the small differences in the definition of the widespread PSNR metric can yield very different results, discussed in more details in our accompanying research paper Aldahdooh et al. (0000). This further underlines the importance of reproducibility to allow comparing different research work with high confidence. To the best of our knowledge, this framework is the first attempt to bring exact reproducibility end-to-end in the context of video quality evaluation research. (C) 2017 The Authors. Published by Elsevier B.V.}},
  author       = {{Aldahdooh, Ahmed and Masala, Enrico and Van Wallendael, Glenn and Barkowsky, Marcus}},
  issn         = {{2352-7110}},
  journal      = {{SOFTWAREX}},
  keywords     = {{Reproducible research,Large database analysis,Video quality}},
  language     = {{eng}},
  pages        = {{64--68}},
  title        = {{Reproducible research framework for objective video quality measures using a large-scale database approach}},
  url          = {{http://dx.doi.org/10.1016/j.softx.2017.09.004}},
  volume       = {{8}},
  year         = {{2018}},
}

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