Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101004(2020)
Blur Image Quality Assessment Method Based on Blur Detection Probability Variation
Figures & Tables(5)
Fig. 2. Comparison of significant effect diagrams.(a)(b) Original images; (c)(d) effect images after SDSP model extraction
Fig. 3. Binarization of salient maps. (a) Original image; (b) salient map; (c) binarization of salient map
Fig. 4. Effect maps of significant area binarization. (a)-(c) Original images; (d)-(f) salient maps
Table 1. Comparison of experimental results
View tableTable 1. Comparison of experimental results
Anamorphose type Algorithm PLCC SROCC OR Gaussian Blur JNBM 0.839 0.8368 0.2471 CPBD 0.9107 0.9124 0.1909 Ref. [8] 0.9030 0.8927 0.2041 Proposed 0.913 0.9231 0.138 JPEG2000 compression JNBM 0.719 0.7255 0.5022 CPBD 0.8835 0.7862 0.326 Ref. [8] 0.7541 0.8096 0.5231 Proposed 0.8932 0.8841 0.2681
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Yuan Zhou, Kai Wang, Haoxiang Zhang, Wenqiang Xu, Long Li. Blur Image Quality Assessment Method Based on Blur Detection Probability Variation[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101004
Paper Information
Category: Image Processing
Received: Aug. 22, 2019
Accepted: Oct. 11, 2019
Published Online: May. 8, 2020
The Author Email: Yuan Zhou (123384007@qq.com), Haoxiang Zhang (mypython3@163.com)
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