No-Reference Quality Assessment of In-Capture Distorted Videos

No-Reference Quality Assessment of In-Capture Distorted Videos

We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (C...

Full description

Bibliographic Details
Main Authors: Mirko Agarla, Luigi Celona, Raimondo Schettini
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Journal of Imaging
Subjects:
video quality assessment
in-capture distortions
convolutional neural network
recurrent neural network
Online Access:https://www.mdpi.com/2313-433X/6/8/74
id doaj-2e91fc2d60be468da72d4b87f70c1832
record_format Article
spelling doaj-2e91fc2d60be468da72d4b87f70c18322020-11-25T03:12:46ZengMDPI AGJournal of Imaging2313-433X2020-07-016747410.3390/jimaging6080074No-Reference Quality Assessment of In-Capture Distorted VideosMirko Agarla0Luigi Celona1Raimondo Schettini2Department of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20126 Milano, ItalyDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20126 Milano, ItalyDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20126 Milano, ItalyWe introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (CNNs) and then estimates the quality score of the whole video by using a Recurrent Neural Network (RNN), which models the temporal information. The extensive experiments conducted on four benchmark databases (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) containing in-capture distortions demonstrate the effectiveness of the proposed method and its ability to generalize in cross-database setup.https://www.mdpi.com/2313-433X/6/8/74video quality assessmentin-capture distortionsconvolutional neural networkrecurrent neural network
collection DOAJ
language English
format Article
sources DOAJ
author Mirko Agarla
Luigi Celona
Raimondo Schettini
spellingShingle Mirko Agarla
Luigi Celona
Raimondo Schettini
No-Reference Quality Assessment of In-Capture Distorted Videos
Journal of Imaging
video quality assessment
in-capture distortions
convolutional neural network
recurrent neural network
author_facet Mirko Agarla
Luigi Celona
Raimondo Schettini
author_sort Mirko Agarla
title No-Reference Quality Assessment of In-Capture Distorted Videos
title_short No-Reference Quality Assessment of In-Capture Distorted Videos
title_full No-Reference Quality Assessment of In-Capture Distorted Videos
title_fullStr No-Reference Quality Assessment of In-Capture Distorted Videos
title_full_unstemmed No-Reference Quality Assessment of In-Capture Distorted Videos
title_sort no-reference quality assessment of in-capture distorted videos
publisher MDPI AG
series Journal of Imaging
issn 2313-433X
publishDate 2020-07-01
description We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (CNNs) and then estimates the quality score of the whole video by using a Recurrent Neural Network (RNN), which models the temporal information. The extensive experiments conducted on four benchmark databases (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) containing in-capture distortions demonstrate the effectiveness of the proposed method and its ability to generalize in cross-database setup.
topic video quality assessment
in-capture distortions
convolutional neural network
recurrent neural network
url https://www.mdpi.com/2313-433X/6/8/74
work_keys_str_mv AT mirkoagarla noreferencequalityassessmentofincapturedistortedvideos
AT luigicelona noreferencequalityassessmentofincapturedistortedvideos
AT raimondoschettini noreferencequalityassessmentofincapturedistortedvideos
_version_ 1724648710558711808

Similar Items