Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images

Nowadays, screen content images (SCIs) are gaining popularity other than natural images (NIs). Quality assessment (QA) methods are needed for these two types of images for better quality of experience. In this thesis, two generalized objective QA methods are proposed for NIs and SCIs, i.e. Curvelet...

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Main Author:	Woei-Tan, Loh
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/38691/8/Thesis%20%28Loh%20Woei%20Tan%29_fulltext.pdf
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id	my-unimas-ir.38691
record_format	uketd_dc
spelling	my-unimas-ir.386912023-10-17T02:58:56Z Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images 2022-06-21 Woei-Tan, Loh QA75 Electronic computers. Computer science Nowadays, screen content images (SCIs) are gaining popularity other than natural images (NIs). Quality assessment (QA) methods are needed for these two types of images for better quality of experience. In this thesis, two generalized objective QA methods are proposed for NIs and SCIs, i.e. Curvelet based Method (CurM) and Edge Magnitude and Direction Method (EMaD). The modelling of a generalized QA method that works for both types of images is complicated since NIs and SCIs have dissimilar statistical properties. Moreover, some properties of NIs and SCIs are conflicting to one another and this makes the modelling more challenging. The proposed methods assess the perceptual quality of an image based on gradient information. For the CurM, the gradient information is extracted through Curvelet transform. The coefficients from Curvelet transform denote the gradient information in terms of magnitude and direction. Different from the usual practice, CurM considers the gradient direction in 360 degree. On the other hand, EMD filters the images with Prewitt kernel to obtain the edge information and direction. Through the filter results, the image is classified into low and high gradient regions. For the high gradient regions, they are filtered again with bigger kernel size. After extracting the gradient information from the two methods, the gradient information extracted from reference and targeted images are compared to compute a similarity score. This score indicates the quality of the targeted image compared to the reference image. From the performance comparison, it is shown that the proposed methods could assess the perceived quality of NIs and SCIs with high accuracy where CurM and EMaD achieve the weighted average of 0.9063 and 0.9124 respectively in Spearman correlation coefficients for LIVE, SIQAD, and SCID databases. Universiti Malaysia Sarawak (UNIMAS) 2022-06 Thesis http://ir.unimas.my/id/eprint/38691/ http://ir.unimas.my/id/eprint/38691/8/Thesis%20%28Loh%20Woei%20Tan%29_fulltext.pdf text en validuser phd doctoral Universiti Malaysia Sarawak Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Woei-Tan, Loh Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
description	Nowadays, screen content images (SCIs) are gaining popularity other than natural images (NIs). Quality assessment (QA) methods are needed for these two types of images for better quality of experience. In this thesis, two generalized objective QA methods are proposed for NIs and SCIs, i.e. Curvelet based Method (CurM) and Edge Magnitude and Direction Method (EMaD). The modelling of a generalized QA method that works for both types of images is complicated since NIs and SCIs have dissimilar statistical properties. Moreover, some properties of NIs and SCIs are conflicting to one another and this makes the modelling more challenging. The proposed methods assess the perceptual quality of an image based on gradient information. For the CurM, the gradient information is extracted through Curvelet transform. The coefficients from Curvelet transform denote the gradient information in terms of magnitude and direction. Different from the usual practice, CurM considers the gradient direction in 360 degree. On the other hand, EMD filters the images with Prewitt kernel to obtain the edge information and direction. Through the filter results, the image is classified into low and high gradient regions. For the high gradient regions, they are filtered again with bigger kernel size. After extracting the gradient information from the two methods, the gradient information extracted from reference and targeted images are compared to compute a similarity score. This score indicates the quality of the targeted image compared to the reference image. From the performance comparison, it is shown that the proposed methods could assess the perceived quality of NIs and SCIs with high accuracy where CurM and EMaD achieve the weighted average of 0.9063 and 0.9124 respectively in Spearman correlation coefficients for LIVE, SIQAD, and SCID databases.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Woei-Tan, Loh
author_facet	Woei-Tan, Loh
author_sort	Woei-Tan, Loh
title	Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
title_short	Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
title_full	Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
title_fullStr	Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
title_full_unstemmed	Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images
title_sort	full-reference edge-based objective quality assessment of natural and screen content images
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Engineering
publishDate	2022
url	http://ir.unimas.my/id/eprint/38691/8/Thesis%20%28Loh%20Woei%20Tan%29_fulltext.pdf
_version_	1783728503972167680

Full-Reference Edge-Based Objective Quality Assessment of Natural and Screen Content Images

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