Blind Stereo Image Quality Evaluation Based on Spatial Domain and Transform Domain Feature Extraction

Yong CHEN; Manli JIN; Kaixin ZHU; Huanlin LIU; Dong CHEN

doi:10.11999/JEIT200694

Volume 43 Issue 10

Oct. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(10): 2958-2966

Yong CHEN, Manli JIN, Kaixin ZHU, Huanlin LIU, Dong CHEN. Blind Stereo Image Quality Evaluation Based on Spatial Domain and Transform Domain Feature Extraction[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2958-2966. doi: 10.11999/JEIT200694

Citation:

Yong CHEN, Manli JIN, Kaixin ZHU, Huanlin LIU, Dong CHEN. Blind Stereo Image Quality Evaluation Based on Spatial Domain and Transform Domain Feature Extraction[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2958-2966. doi: 10.11999/JEIT200694

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Blind Stereo Image Quality Evaluation Based on Spatial Domain and Transform Domain Feature Extraction

doi: 10.11999/JEIT200694

1.
Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (51977021)

Received Date: 2020-08-06
Rev Recd Date: 2021-07-23

Available Online: 2021-08-27

Publish Date: 2021-10-18

Abstract

Abstract

For the problem of insufficient accuracy of stereo image quality prediction, a blind stereoscopic image quality assessment model combining spatial domain and transform domain to extract quality-aware features is proposed. Firstly, the statistical features of the natural scenes in the left and right views are extracted respectively in space domain and transformation domain, and statistical features of natural scenes from synthetic monocular images is extracted in transformation domain. Finally, Support Vector Regression (SVR) is used to train a stereoscopic image quality evaluation model from the feature domain to the quality score domain, so as to establish SIQA objective quality evaluation model. The performance of the proposed method is compared with some state-of-the-art full-reference, reduced-reference and no-reference stereoscopic image quality evaluation algorithms on the four public stereo image databases, taking the performance test in live 3D phase I image library as an example. SROCC of 0.967, PLCC of 0.946 and RMSE of 5.603 are achieved, which verifies the effectiveness of the proposed algorithm.
- Stereoscopic image,
- No reference,
- Spatial domain characteristics,
- Transform domain features

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References(30)

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