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No-reference synthetic image quality assessment with convolutional neural network and local image saliency

Wang, Xiaochuan; Liang, Xiaohui; Yang, Bailin; Li, Frederick W.B.

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Authors

Xiaochuan Wang

Xiaohui Liang

Bailin Yang



Abstract

Depth-image-based rendering (DIBR) is widely used in 3DTV, free-viewpoint video, and interactive 3D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, particularly geometric distortions induced by object dis-occlusion. Ensuring the quality of synthetic images is critical to maintaining adequate system service. However, traditional 2D image quality metrics are ineffective for evaluating synthetic images as they are not sensitive to geometric distortion. In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights. Due to the lack of existing training data, we construct a new DIBR synthetic image dataset as part of our contribution. Experiments were conducted on both the public benchmark IRCCyN/IVC DIBR image dataset and our own dataset. Results demonstrate that our proposed metric outperforms traditional 2D image quality metrics and state-of-the-art DIBR-related metrics.

Citation

Wang, X., Liang, X., Yang, B., & Li, F. W. (2019). No-reference synthetic image quality assessment with convolutional neural network and local image saliency. Computational Visual Media, 5(2), 193-208. https://doi.org/10.1007/s41095-019-0131-6

Journal Article Type Article
Acceptance Date Jan 27, 2019
Online Publication Date Mar 30, 2019
Publication Date Jun 1, 2019
Deposit Date Jul 12, 2019
Publicly Available Date Jul 12, 2019
Journal Computational Visual Media
Print ISSN 2096-0433
Electronic ISSN 2096-0662
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 5
Issue 2
Pages 193-208
DOI https://doi.org/10.1007/s41095-019-0131-6

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© The Author(s) 2019



Open Access
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.





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