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No-Reference Stereo Image Quality Assessment Based on Transfer Learning

Lixiu Wu1,*, Song Wang2, Qingbing Sang3

1 Jiangsu Key Construction Laboratory of IoT Application Technology (Wuxi Taihu University), Wuxi, 214000, China
2 Pactera Yuanhui Technology (Wuxi) Co., LTD., Wuxi, 214000, China
3 Jiangnan University, Wuxi, 214000, China

* Corresponding Author: Lixiu Wu. Email: email

Journal of New Media 2022, 4(3), 125-135. https://doi.org/10.32604/jnm.2022.027199

Abstract

In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the input image is preprocessed by phase congruency transformation, which further improves the performance of the algorithm. The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers. The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.

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Cite This Article

APA Style
Wu, L., Wang, S., Sang, Q. (2022). No-reference stereo image quality assessment based on transfer learning. Journal of New Media, 4(3), 125-135. https://doi.org/10.32604/jnm.2022.027199
Vancouver Style
Wu L, Wang S, Sang Q. No-reference stereo image quality assessment based on transfer learning. J New Media . 2022;4(3):125-135 https://doi.org/10.32604/jnm.2022.027199
IEEE Style
L. Wu, S. Wang, and Q. Sang "No-Reference Stereo Image Quality Assessment Based on Transfer Learning," J. New Media , vol. 4, no. 3, pp. 125-135. 2022. https://doi.org/10.32604/jnm.2022.027199



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