Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers

Autores
Pérez, Carlos A.
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Current browsers have sophisticated execution environments for Javascript, and fast rendering engines. With the advent of HTML5, they accept digital cameras, and they can process, in real time, video streaming between browsers, allowing instant communications. In addition, the introduction of the low-level virtual machine (LLVM) allows image-processing libraries to be delivered, alongside web pages, as specialized scripts that execute in browser, with significant speed gains when compared to traditional Javascript engines. This make the browser a very suitable platform to deliver web applications with heavy image processing tasks, that execute at native speeds. However, measuring such performance in modern browsers is a demanding challenge. In this paper, a set of recommended practices to use and to benchmark Opencv.js are presented and obtained figures on several testbeds are discussed. Measurements involved a desktop PC, a selection of smartphones with mainstream processors, and a Raspberry Pi single-board computer, which resulted in several findings that confirm the maturity of mobile an embedded browser for image-processing with Javascript at client side, running at native speeds.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Devices
Javascript
Web assembly
OpenCV
Performance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/89186
Acceder
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network_name_str SEDICI (UNLP)
spelling Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsersPérez, Carlos A.Ciencias InformáticasDevicesJavascriptWeb assemblyOpenCVPerformanceCurrent browsers have sophisticated execution environments for Javascript, and fast rendering engines. With the advent of HTML5, they accept digital cameras, and they can process, in real time, video streaming between browsers, allowing instant communications. In addition, the introduction of the low-level virtual machine (LLVM) allows image-processing libraries to be delivered, alongside web pages, as specialized scripts that execute in browser, with significant speed gains when compared to traditional Javascript engines. This make the browser a very suitable platform to deliver web applications with heavy image processing tasks, that execute at native speeds. However, measuring such performance in modern browsers is a demanding challenge. In this paper, a set of recommended practices to use and to benchmark Opencv.js are presented and obtained figures on several testbeds are discussed. Measurements involved a desktop PC, a selection of smartphones with mainstream processors, and a Raspberry Pi single-board computer, which resulted in several findings that confirm the maturity of mobile an embedded browser for image-processing with Javascript at client side, running at native speeds.Sociedad Argentina de Informática e Investigación Operativa2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf51-57http://sedici.unlp.edu.ar/handle/10915/89186enginfo:eu-repo/semantics/altIdentifier/issn/2683-8990info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:18:18Zoai:sedici.unlp.edu.ar:10915/89186Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:18:19.048SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
title Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
spellingShingle Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
Pérez, Carlos A.
Ciencias Informáticas
Devices
Javascript
Web assembly
OpenCV
Performance
title_short Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
title_full Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
title_fullStr Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
title_full_unstemmed Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
title_sort Measuring Opencv.js performance with Wasm execution engine in desktop, embedded and mobile browsers
dc.creator.none.fl_str_mv Pérez, Carlos A.
author Pérez, Carlos A.
author_facet Pérez, Carlos A.
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Devices
Javascript
Web assembly
OpenCV
Performance
topic Ciencias Informáticas
Devices
Javascript
Web assembly
OpenCV
Performance
dc.description.none.fl_txt_mv Current browsers have sophisticated execution environments for Javascript, and fast rendering engines. With the advent of HTML5, they accept digital cameras, and they can process, in real time, video streaming between browsers, allowing instant communications. In addition, the introduction of the low-level virtual machine (LLVM) allows image-processing libraries to be delivered, alongside web pages, as specialized scripts that execute in browser, with significant speed gains when compared to traditional Javascript engines. This make the browser a very suitable platform to deliver web applications with heavy image processing tasks, that execute at native speeds. However, measuring such performance in modern browsers is a demanding challenge. In this paper, a set of recommended practices to use and to benchmark Opencv.js are presented and obtained figures on several testbeds are discussed. Measurements involved a desktop PC, a selection of smartphones with mainstream processors, and a Raspberry Pi single-board computer, which resulted in several findings that confirm the maturity of mobile an embedded browser for image-processing with Javascript at client side, running at native speeds.
Sociedad Argentina de Informática e Investigación Operativa
description Current browsers have sophisticated execution environments for Javascript, and fast rendering engines. With the advent of HTML5, they accept digital cameras, and they can process, in real time, video streaming between browsers, allowing instant communications. In addition, the introduction of the low-level virtual machine (LLVM) allows image-processing libraries to be delivered, alongside web pages, as specialized scripts that execute in browser, with significant speed gains when compared to traditional Javascript engines. This make the browser a very suitable platform to deliver web applications with heavy image processing tasks, that execute at native speeds. However, measuring such performance in modern browsers is a demanding challenge. In this paper, a set of recommended practices to use and to benchmark Opencv.js are presented and obtained figures on several testbeds are discussed. Measurements involved a desktop PC, a selection of smartphones with mainstream processors, and a Raspberry Pi single-board computer, which resulted in several findings that confirm the maturity of mobile an embedded browser for image-processing with Javascript at client side, running at native speeds.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/89186
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2683-8990
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
51-57
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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