No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortio...

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Autor principal:	Domonkos Varga
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	no-reference video quality assessment Benford’s law feature extraction Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/271c366c6e3a4a96a8b08e9f2c32dd1b
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id	oai:doaj.org-article:271c366c6e3a4a96a8b08e9f2c32dd1b
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spelling	oai:doaj.org-article:271c366c6e3a4a96a8b08e9f2c32dd1b2021-11-25T17:24:28ZNo-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features10.3390/electronics102227682079-9292https://doaj.org/article/271c366c6e3a4a96a8b08e9f2c32dd1b2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2768https://doaj.org/toc/2079-9292No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.Domonkos VargaMDPI AGarticleno-reference video quality assessmentBenford’s lawfeature extractionElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2768, p 2768 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	no-reference video quality assessment Benford’s law feature extraction Electronics TK7800-8360
spellingShingle	no-reference video quality assessment Benford’s law feature extraction Electronics TK7800-8360 Domonkos Varga No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
description	No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.
format	article
author	Domonkos Varga
author_facet	Domonkos Varga
author_sort	Domonkos Varga
title	No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
title_short	No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
title_full	No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
title_fullStr	No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
title_full_unstemmed	No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
title_sort	no-reference video quality assessment based on benford’s law and perceptual features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/271c366c6e3a4a96a8b08e9f2c32dd1b
work_keys_str_mv	AT domonkosvarga noreferencevideoqualityassessmentbasedonbenfordslawandperceptualfeatures
_version_	1718412396992659456

No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

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