Objective blur assessment based on contraction errors of local contrast maps
Blur distortion appears in multimedia content due to acquisition, compression or transmission errors. In this paper, a method is proposed to predict blur severity based on the contraction errors of local contrast maps. The proposed method is developed from the observation that histogram distributio...
Saved in:
Main Authors: | , |
---|---|
Format: | E-Article |
Language: | English |
Published: |
Springer
2015
|
Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/11964/1/No%2011%20%28abstrak%29.pdf http://ir.unimas.my/id/eprint/11964/ http://www.springer.com/computer/information+systems+and+applications/journal/11042 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.unimas.ir.11964 |
---|---|
record_format |
eprints |
spelling |
my.unimas.ir.119642016-10-21T07:21:46Z http://ir.unimas.my/id/eprint/11964/ Objective blur assessment based on contraction errors of local contrast maps Bong, David Boon Liang Bee, Ee Khoo QC Physics Blur distortion appears in multimedia content due to acquisition, compression or transmission errors. In this paper, a method is proposed to predict blur severity based on the contraction errors of local contrast maps. The proposed method is developed from the observation that histogram distribution of natural image would contract according to the degree of blur distortion. In order to quantify the level of contraction, an efficient method of determining local contrast boundaries is used. The upper and lower bounds of local histogram distribution are defined for the original image, and outlying points beyond these bounds are used to form the local contrast map. For the corresponding patch of a blur image, the same values of upper and lower bounds are used and the local contrast map for the blur image could be produced. Total difference between local contrast maps of the original and blur images is the contraction errors which are used to derive the blur score. The proposed method has advantages in terms of computation efficiency, and is performed in the spatial domain without the need of data transformation, conversion or filtering. In addition, prior training is not required at all for the model. Implementation of the proposed method as a multimedia tool is useful for estimating blur severity in multimedia content. The performance of the proposed method is verified by using three different blur databases and compared to popular state-of-the-artmethods. Experiment results show that the proposed blur metric has high correlation with human perception of blurriness. Springer 2015-04-23 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/11964/1/No%2011%20%28abstrak%29.pdf Bong, David Boon Liang and Bee, Ee Khoo (2015) Objective blur assessment based on contraction errors of local contrast maps. Multimedia Tools and Applications, 74 (17). pp. 7355-7378. ISSN 1380-7501 http://www.springer.com/computer/information+systems+and+applications/journal/11042 DOI 10.1007/s11042-014-1983-5 |
institution |
Universiti Malaysia Sarawak |
building |
Centre for Academic Information Services (CAIS) |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Sarawak |
content_source |
UNIMAS Institutional Repository |
url_provider |
http://ir.unimas.my/ |
language |
English |
topic |
QC Physics |
spellingShingle |
QC Physics Bong, David Boon Liang Bee, Ee Khoo Objective blur assessment based on contraction errors of local contrast maps |
description |
Blur distortion appears in multimedia content due to acquisition, compression or transmission errors. In this paper, a method is proposed to predict blur severity based on the contraction errors of local contrast maps. The proposed method is developed from the observation
that histogram distribution of natural image would contract according to the degree of blur distortion. In order to quantify the level of contraction, an efficient method of determining local contrast boundaries is used. The upper and lower bounds of local histogram distribution are defined for the original image, and outlying points beyond these bounds are used to form the local contrast map. For the corresponding patch of a blur image, the same values of upper and lower bounds are used and the local contrast map for the blur image could be produced. Total difference between local contrast maps of the original and blur images is the contraction errors which are used to derive the blur score. The proposed method has advantages in terms of computation efficiency, and is performed in the spatial domain without the need of data transformation, conversion or filtering. In addition, prior training is not required at all for the model. Implementation of the proposed method as a multimedia tool is useful for estimating blur severity in multimedia content. The performance of the proposed method is verified by using three different blur databases and compared to popular state-of-the-artmethods. Experiment results show that the proposed blur metric has high correlation with human perception of blurriness. |
format |
E-Article |
author |
Bong, David Boon Liang Bee, Ee Khoo |
author_facet |
Bong, David Boon Liang Bee, Ee Khoo |
author_sort |
Bong, David Boon Liang |
title |
Objective blur assessment based on contraction errors of local contrast maps |
title_short |
Objective blur assessment based on contraction errors of local contrast maps |
title_full |
Objective blur assessment based on contraction errors of local contrast maps |
title_fullStr |
Objective blur assessment based on contraction errors of local contrast maps |
title_full_unstemmed |
Objective blur assessment based on contraction errors of local contrast maps |
title_sort |
objective blur assessment based on contraction errors of local contrast maps |
publisher |
Springer |
publishDate |
2015 |
url |
http://ir.unimas.my/id/eprint/11964/1/No%2011%20%28abstrak%29.pdf http://ir.unimas.my/id/eprint/11964/ http://www.springer.com/computer/information+systems+and+applications/journal/11042 |
_version_ |
1644511311520858112 |
score |
13.18916 |