Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

PhiCong, H; Perry, S; Cheng, E; HoangVan, X

Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

PhiCong, H Perry, S

Cheng, E HoangVan, X

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Electronics, 11, (5), pp. 759-759

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Field	Value	Language
dc.contributor.author	PhiCong, H
dc.contributor.author	Perry, S https://orcid.org/0000-0002-2794-3178
dc.contributor.author	Cheng, E
dc.contributor.author	HoangVan, X
dc.date.accessioned	2022-03-02T20:42:51Z
dc.date.available	2022-03-02T20:42:51Z
dc.identifier.citation	Electronics, 11, (5), pp. 759-759
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/154975
dc.description.abstract	<jats:p>Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Electronics
dc.relation.isbasedon	10.3390/electronics11050759
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dc.subject	0906 Electrical and Electronic Engineering
dc.title	Objective Quality Assessment Metrics for Light Field Image Based on Textural Features
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2022-03-02T20:42:50Z
pubs.issue	5
pubs.publication-status	Published online
pubs.volume	11
utslib.citation.issue	5

Abstract:

Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).

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http://hdl.handle.net/10453/154975