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 |
|