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Stereoscopic video quality assessment based on 3D convolutional neural networks.pdf (1.52 MB)

Stereoscopic video quality assessment based on 3D convolutional neural networks

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journal contribution
posted on 2018-09-10, 10:02 authored by Jiachen Yang, Yinghao Zhu, Chaofan Ma, Wen Lu, Qinggang MengQinggang Meng
The research of stereoscopic video quality assessment (SVQA) plays an important role for promoting the development of stereoscopic video system. Existing SVQA metrics rely on hand-crafted features, which is inaccurate and time-consuming because of the diversity and complexity of stereoscopic video distortion. This paper introduces a 3D convolutional neural networks (CNN) based SVQA framework that can model not only local spatio-temporal information but also global temporal information with cubic difference video patches as input. First, instead of using hand-crafted features, we design a 3D CNN architecture to automatically and effectively capture local spatio-temporal features. Then we employ a quality score fusion strategy considering global temporal clues to obtain final video-level predicted score. Extensive experiments conducted on two public stereoscopic video quality datasets show that the proposed method correlates highly with human perception and outperforms state-of-the-art methods by a large margin. We also show that our 3D CNN features have more desirable property for SVQA than hand-crafted features in previous methods, and our 3D CNN features together with support vector regression (SVR) can further boost the performance. In addition, with no complex preprocessing and GPU acceleration, our proposed method is demonstrated computationally efficient and easy to use.

Funding

This work was supported by the Foundation of Pre-Research on Equipment of China (No.61403120103) and the National Natural Science Foundation of China (Nos. 61372130, 61432014).

History

School

  • Science

Department

  • Computer Science

Published in

Neurocomputing

Citation

YANG, J. ... et al, 2018. Stereoscopic video quality assessment based on 3D convolutional neural networks. Neurocomputing, 309, pp. 83-93.

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2018-04-30

Publication date

2018

Notes

This paper was accepted for publication in the journal Neurocomputing and the definitive published version is available at https://doi.org/10.1016/j.neucom.2018.04.072.

ISSN

0925-2312

Language

  • en