A novel quality metric using spatiotemporal correlational data of human eye maneuver

Pallab Kanti Podder; Manoranjan Paul; Manzur Murshed

doi:10.1109/DICTA.2017.8227396

A novel quality metric using spatiotemporal correlational data of human eye maneuver

Pallab Kanti Podder, Manoranjan Paul, Manzur Murshed

Federation University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

The popularly used subjective estimator - mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no-reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants’ eye-tracker recorded gaze-data indicate more concentrated eye traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.

Original language	English
Title of host publication	Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA)
Subtitle of host publication	Techniques and Applications
Editors	Yi Guo, Hongdong Li, Weidong Cai, Manzur Murshed, Zhiyong Wang, Junbin Gao, David Dagan Feng
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781538628393, 9781538628386
ISBN (Print)	9781538628409 (Print on demand)
DOIs	https://doi.org/10.1109/DICTA.2017.8227396
Publication status	Published - 21 Dec 2017
Event	2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Novotel Sydney Manly Pacific, Sydney, Australia Duration: 29 Nov 2017 → 01 Dec 2017 http://dicta2017.dictaconference.org/index.html (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

Conference

Conference	2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Country/Territory	Australia
City	Sydney
Period	29/11/17 → 01/12/17
Other	The International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS).
Internet address	http://dicta2017.dictaconference.org/index.html (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

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10.1109/DICTA.2017.8227396

Cite this

Podder, P. K., Paul, M., & Murshed, M. (2017). A novel quality metric using spatiotemporal correlational data of human eye maneuver. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. D. Feng (Eds.), Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA): Techniques and Applications (pp. 1-8). Article 8227396 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2017.8227396

Podder, Pallab Kanti ; Paul, Manoranjan ; Murshed, Manzur. / A novel quality metric using spatiotemporal correlational data of human eye maneuver. Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA): Techniques and Applications. editor / Yi Guo ; Hongdong Li ; Weidong Cai ; Manzur Murshed ; Zhiyong Wang ; Junbin Gao ; David Dagan Feng. United States : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1-8

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title = "A novel quality metric using spatiotemporal correlational data of human eye maneuver",

abstract = "The popularly used subjective estimator - mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no-reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants{\textquoteright} eye-tracker recorded gaze-data indicate more concentrated eye traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS. ",

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author = "Podder, {Pallab Kanti} and Manoranjan Paul and Manzur Murshed",

note = "Includes bibliographical references.; 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) ; Conference date: 29-11-2017 Through 01-12-2017",

year = "2017",

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doi = "10.1109/DICTA.2017.8227396",

language = "English",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Podder, PK, Paul, M & Murshed, M 2017, A novel quality metric using spatiotemporal correlational data of human eye maneuver. in Y Guo, H Li, W Cai, M Murshed, Z Wang, J Gao & DD Feng (eds), Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA): Techniques and Applications., 8227396, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-8, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, New South Wales, Australia, 29/11/17. https://doi.org/10.1109/DICTA.2017.8227396

A novel quality metric using spatiotemporal correlational data of human eye maneuver. / Podder, Pallab Kanti; Paul, Manoranjan; Murshed, Manzur.
Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA): Techniques and Applications. ed. / Yi Guo; Hongdong Li; Weidong Cai; Manzur Murshed; Zhiyong Wang; Junbin Gao; David Dagan Feng. United States: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-8 8227396.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - A novel quality metric using spatiotemporal correlational data of human eye maneuver

AU - Podder, Pallab Kanti

AU - Paul, Manoranjan

AU - Murshed, Manzur

N1 - Includes bibliographical references.

PY - 2017/12/21

Y1 - 2017/12/21

N2 - The popularly used subjective estimator - mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no-reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants’ eye-tracker recorded gaze-data indicate more concentrated eye traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.

AB - The popularly used subjective estimator - mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no-reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants’ eye-tracker recorded gaze-data indicate more concentrated eye traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.

KW - Eye-traversal

KW - Eye-tracking

KW - Gaze-trajectory

KW - HEVC

KW - MOS

KW - QMET

KW - Quality assessment

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U2 - 10.1109/DICTA.2017.8227396

DO - 10.1109/DICTA.2017.8227396

M3 - Conference paper

SN - 9781538628409 (Print on demand)

SP - 1

EP - 8

BT - Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA)

A2 - Guo, Yi

A2 - Li, Hongdong

A2 - Cai, Weidong

A2 - Murshed, Manzur

A2 - Wang, Zhiyong

A2 - Gao, Junbin

A2 - Feng, David Dagan

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

Y2 - 29 November 2017 through 1 December 2017

ER -

Podder PK, Paul M, Murshed M. A novel quality metric using spatiotemporal correlational data of human eye maneuver. In Guo Y, Li H, Cai W, Murshed M, Wang Z, Gao J, Feng DD, editors, Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications (DICTA): Techniques and Applications. United States: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1-8. 8227396 doi: 10.1109/DICTA.2017.8227396

A novel quality metric using spatiotemporal correlational data of human eye maneuver

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