Design of Video Quality Metrics with Multi-Way Data Analysis: A data driven approach - Brossura

Keimel, Christian

 
9789811091193: Design of Video Quality Metrics with Multi-Way Data Analysis: A data driven approach

Sinossi

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. 


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Dalla quarta di copertina

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. 

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9789811002687: Design of Video Quality Metrics With Multi-way Data Analysis: A data driven approach

Edizione in evidenza

ISBN 10:  9811002681 ISBN 13:  9789811002687
Casa editrice: Springer Nature, 2016
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