A parametric analysis of AVA to optimise Netflix performance

In this research study, researcher tries to understand how Over-the-top platforms like Netflix categorically utilize aesthetic visual analysis (AVA); an image selection tool to reduce time and increase efficacy, through a parametric analysis of AVA to optimise Netflix performance. This research pape...

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Detalles Bibliográficos
Autores principales: Rastogi, Divya, Parihar, Tasha Singh, Kumar, Harish
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186304/
https://www.ncbi.nlm.nih.gov/pubmed/37360316
http://dx.doi.org/10.1007/s41870-023-01281-z
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author Rastogi, Divya
Parihar, Tasha Singh
Kumar, Harish
author_facet Rastogi, Divya
Parihar, Tasha Singh
Kumar, Harish
author_sort Rastogi, Divya
collection PubMed
description In this research study, researcher tries to understand how Over-the-top platforms like Netflix categorically utilize aesthetic visual analysis (AVA); an image selection tool to reduce time and increase efficacy, through a parametric analysis of AVA to optimise Netflix performance. This research paper tries to answer all the questions related to the how the database of aesthetic visual analysis (AVA), an image selection tool works better or more like humans. To further substantiate the popularity of Netflix, a real time data of 307 respondents who use OTT platforms in Delhi was collected to determine whether Netflix in fact is or not the market leader. 63.8% of them selected Netflix as their top option.
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spelling pubmed-101863042023-05-17 A parametric analysis of AVA to optimise Netflix performance Rastogi, Divya Parihar, Tasha Singh Kumar, Harish Int J Inf Technol Original Research In this research study, researcher tries to understand how Over-the-top platforms like Netflix categorically utilize aesthetic visual analysis (AVA); an image selection tool to reduce time and increase efficacy, through a parametric analysis of AVA to optimise Netflix performance. This research paper tries to answer all the questions related to the how the database of aesthetic visual analysis (AVA), an image selection tool works better or more like humans. To further substantiate the popularity of Netflix, a real time data of 307 respondents who use OTT platforms in Delhi was collected to determine whether Netflix in fact is or not the market leader. 63.8% of them selected Netflix as their top option. Springer Nature Singapore 2023-05-16 /pmc/articles/PMC10186304/ /pubmed/37360316 http://dx.doi.org/10.1007/s41870-023-01281-z Text en © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Rastogi, Divya
Parihar, Tasha Singh
Kumar, Harish
A parametric analysis of AVA to optimise Netflix performance
title A parametric analysis of AVA to optimise Netflix performance
title_full A parametric analysis of AVA to optimise Netflix performance
title_fullStr A parametric analysis of AVA to optimise Netflix performance
title_full_unstemmed A parametric analysis of AVA to optimise Netflix performance
title_short A parametric analysis of AVA to optimise Netflix performance
title_sort parametric analysis of ava to optimise netflix performance
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186304/
https://www.ncbi.nlm.nih.gov/pubmed/37360316
http://dx.doi.org/10.1007/s41870-023-01281-z
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