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A deep evaluator for image retargeting quality by geometrical and contextual interaction

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journal contribution
posted on 10.09.2018, 10:39 authored by Bin Jiang, Jiachen Yang, Qinggang MengQinggang Meng, Baihua Li, Wen Lu
An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61471260, and in part by the Natural Science Foundation of Tianjin under Grant 16JCYBJC16000.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Cybernetics

Volume

50

Issue

1

Pages

87 - 99

Citation

JIANG, B. ... et al, 2018. A deep evaluator for image retargeting quality by geometrical and contextual interaction. IEEE Transactions on Cybernetics, 50(1), pp. 87 - 99.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Acceptance date

02/08/2018

Publication date

2018-09-03

Copyright date

2020

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

2168-2267

eISSN

2168-2275

Language

en