Image quality assessment based on gradient similarity

In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use th...

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Main Authors: Liu, Anmin, Lin, Weisi, Narwaria, Manish
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/99259
http://hdl.handle.net/10220/13525
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-992592020-05-28T07:17:56Z Image quality assessment based on gradient similarity Liu, Anmin Lin, Weisi Narwaria, Manish School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes. 2013-09-19T01:35:41Z 2019-12-06T20:05:09Z 2013-09-19T01:35:41Z 2019-12-06T20:05:09Z 2011 2011 Journal Article Liu, A., Lin, W., & Narwaria, M. (2011). Image quality assessment based on gradient similarity. IEEE transactions on image processing, 21(4), 1500-1512. 1057-7149 https://hdl.handle.net/10356/99259 http://hdl.handle.net/10220/13525 10.1109/TIP.2011.2175935 en IEEE transactions on image processing © 2011 IEEE
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Liu, Anmin
Lin, Weisi
Narwaria, Manish
Image quality assessment based on gradient similarity
description In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Liu, Anmin
Lin, Weisi
Narwaria, Manish
format Article
author Liu, Anmin
Lin, Weisi
Narwaria, Manish
author_sort Liu, Anmin
title Image quality assessment based on gradient similarity
title_short Image quality assessment based on gradient similarity
title_full Image quality assessment based on gradient similarity
title_fullStr Image quality assessment based on gradient similarity
title_full_unstemmed Image quality assessment based on gradient similarity
title_sort image quality assessment based on gradient similarity
publishDate 2013
url https://hdl.handle.net/10356/99259
http://hdl.handle.net/10220/13525
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