An image quality assessment algorithm based on feature selection

Ting Lu; Yanning Zhang; Haisen Li

doi:10.1007/978-3-642-36669-7_36

An image quality assessment algorithm based on feature selection

Ting Lu, Yanning Zhang, Haisen Li

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Image Quality Assessment(IQA) is of fundamental importance to numerous imaging and video processing applications. For most of the applications, the perceptual meaningful measure is the one which can automatically assess the quality of images or videos in a perceptually consistent manner. However, most commonly used IQA metrics are not consistent well with the human judgments of image quality. Recently, the SSIM metric which takes people's visual characteristics into consideration performs much better than the traditional PSNR/MSE. But the defects of it still exit on some specific kinds of distortions. A new algorithm of IQA based on feature selection is proposed in this paper. Local gradient entropy and phase congruency are added to the SSIM framework. Through in-depth feature selection and definition plus better pooling strategy, this algorithm performs much better in LIVE datasets.

Original language	English
Title of host publication	Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers
Pages	289-297
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-36669-7_36
State	Published - 2013
Event	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 - Nanjing, China Duration: 15 Oct 2012 → 17 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7751 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012
Country/Territory	China
City	Nanjing
Period	15/10/12 → 17/10/12

Keywords

Feature selection
Image quality assessment
Local gradient entropy
Phase congruency

Access to Document

10.1007/978-3-642-36669-7_36

Cite this

Lu, T., Zhang, Y., & Li, H. (2013). An image quality assessment algorithm based on feature selection. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers (pp. 289-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS). https://doi.org/10.1007/978-3-642-36669-7_36

Lu, Ting ; Zhang, Yanning ; Li, Haisen. / An image quality assessment algorithm based on feature selection. Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. pp. 289-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f2dfd97f2ea4444e8f3cf9822f35d86d,

title = "An image quality assessment algorithm based on feature selection",

abstract = "Image Quality Assessment(IQA) is of fundamental importance to numerous imaging and video processing applications. For most of the applications, the perceptual meaningful measure is the one which can automatically assess the quality of images or videos in a perceptually consistent manner. However, most commonly used IQA metrics are not consistent well with the human judgments of image quality. Recently, the SSIM metric which takes people's visual characteristics into consideration performs much better than the traditional PSNR/MSE. But the defects of it still exit on some specific kinds of distortions. A new algorithm of IQA based on feature selection is proposed in this paper. Local gradient entropy and phase congruency are added to the SSIM framework. Through in-depth feature selection and definition plus better pooling strategy, this algorithm performs much better in LIVE datasets.",

keywords = "Feature selection, Image quality assessment, Local gradient entropy, Phase congruency",

author = "Ting Lu and Yanning Zhang and Haisen Li",

year = "2013",

doi = "10.1007/978-3-642-36669-7_36",

language = "英语",

isbn = "9783642366680",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "289--297",

booktitle = "Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers",

note = "3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 ; Conference date: 15-10-2012 Through 17-10-2012",

}

Lu, T, Zhang, Y & Li, H 2013, An image quality assessment algorithm based on feature selection. in Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7751 LNCS, pp. 289-297, 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012, Nanjing, China, 15/10/12. https://doi.org/10.1007/978-3-642-36669-7_36

An image quality assessment algorithm based on feature selection. / Lu, Ting; Zhang, Yanning; Li, Haisen.
Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 289-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An image quality assessment algorithm based on feature selection

AU - Lu, Ting

AU - Zhang, Yanning

AU - Li, Haisen

PY - 2013

Y1 - 2013

N2 - Image Quality Assessment(IQA) is of fundamental importance to numerous imaging and video processing applications. For most of the applications, the perceptual meaningful measure is the one which can automatically assess the quality of images or videos in a perceptually consistent manner. However, most commonly used IQA metrics are not consistent well with the human judgments of image quality. Recently, the SSIM metric which takes people's visual characteristics into consideration performs much better than the traditional PSNR/MSE. But the defects of it still exit on some specific kinds of distortions. A new algorithm of IQA based on feature selection is proposed in this paper. Local gradient entropy and phase congruency are added to the SSIM framework. Through in-depth feature selection and definition plus better pooling strategy, this algorithm performs much better in LIVE datasets.

AB - Image Quality Assessment(IQA) is of fundamental importance to numerous imaging and video processing applications. For most of the applications, the perceptual meaningful measure is the one which can automatically assess the quality of images or videos in a perceptually consistent manner. However, most commonly used IQA metrics are not consistent well with the human judgments of image quality. Recently, the SSIM metric which takes people's visual characteristics into consideration performs much better than the traditional PSNR/MSE. But the defects of it still exit on some specific kinds of distortions. A new algorithm of IQA based on feature selection is proposed in this paper. Local gradient entropy and phase congruency are added to the SSIM framework. Through in-depth feature selection and definition plus better pooling strategy, this algorithm performs much better in LIVE datasets.

KW - Feature selection

KW - Image quality assessment

KW - Local gradient entropy

KW - Phase congruency

UR - http://www.scopus.com/inward/record.url?scp=84892930628&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-36669-7_36

DO - 10.1007/978-3-642-36669-7_36

M3 - 会议稿件

AN - SCOPUS:84892930628

SN - 9783642366680

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 289

EP - 297

BT - Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers

T2 - 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

Y2 - 15 October 2012 through 17 October 2012

ER -

Lu T, Zhang Y, Li H. An image quality assessment algorithm based on feature selection. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 289-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-36669-7_36

An image quality assessment algorithm based on feature selection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this