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Design of video quality metrics with multi-way data analysis: a data driven approach
This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but...
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Lenguaje: | eng |
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Springer
2016
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Acceso en línea: | https://dx.doi.org/10.1007/978-981-10-0269-4 http://cds.cern.ch/record/2120188 |
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author | Keimel, Christian |
author_facet | Keimel, Christian |
author_sort | Keimel, Christian |
collection | CERN |
description | This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling. . |
id | cern-2120188 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
publisher | Springer |
record_format | invenio |
spelling | cern-21201882021-04-21T19:56:09Zdoi:10.1007/978-981-10-0269-4http://cds.cern.ch/record/2120188engKeimel, ChristianDesign of video quality metrics with multi-way data analysis: a data driven approachEngineeringThis book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling. .Springeroai:cds.cern.ch:21201882016 |
spellingShingle | Engineering Keimel, Christian Design of video quality metrics with multi-way data analysis: a data driven approach |
title | Design of video quality metrics with multi-way data analysis: a data driven approach |
title_full | Design of video quality metrics with multi-way data analysis: a data driven approach |
title_fullStr | Design of video quality metrics with multi-way data analysis: a data driven approach |
title_full_unstemmed | Design of video quality metrics with multi-way data analysis: a data driven approach |
title_short | Design of video quality metrics with multi-way data analysis: a data driven approach |
title_sort | design of video quality metrics with multi-way data analysis: a data driven approach |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-981-10-0269-4 http://cds.cern.ch/record/2120188 |
work_keys_str_mv | AT keimelchristian designofvideoqualitymetricswithmultiwaydataanalysisadatadrivenapproach |