Efficient, robust and divisible paired comparison for subjective quality assessment

Efficient, robust and divisible paired comparison for subjective quality assessment The scale of a database is important for machine learning based image and video quality assessment. Nevertheless, it is greatly limited by the subjective test method. Among the various methods, Paired Comparison(PC) is acknowledged as the most reliable one. However, the test duration grows with square of the number of samples. To solve the dilemma, we propose an improved paired comparison method in this paper. Three types of priori are incorporated to cut down the test duration, including the long-term priori as experience results condensed in existing quality metric, the short-term priori as the subjective scores calculated by the predecessor in ongoing session, and the dynamic priori as the previous decision made by the current assessor. Based on these priori knowledge, only indispensable part of decision is needed to be made by the assessor. Equivalent performance could be achieved in one-tenth of the time used in full paired comparison method. While it is robust to mis-click and divisible to expand the database to a large scale. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Efficient, robust and divisible paired comparison for subjective quality assessment

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4977-2
Publisher site
See Article on Publisher Site

Abstract

The scale of a database is important for machine learning based image and video quality assessment. Nevertheless, it is greatly limited by the subjective test method. Among the various methods, Paired Comparison(PC) is acknowledged as the most reliable one. However, the test duration grows with square of the number of samples. To solve the dilemma, we propose an improved paired comparison method in this paper. Three types of priori are incorporated to cut down the test duration, including the long-term priori as experience results condensed in existing quality metric, the short-term priori as the subjective scores calculated by the predecessor in ongoing session, and the dynamic priori as the previous decision made by the current assessor. Based on these priori knowledge, only indispensable part of decision is needed to be made by the assessor. Equivalent performance could be achieved in one-tenth of the time used in full paired comparison method. While it is robust to mis-click and divisible to expand the database to a large scale.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 5, 2017

References

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