Sparse Representation-Based Full-Reference Quality Assessment of Distorted Satellite Stereo Images

Yiming Xiong; Feng Shao; Xiangchao Meng

doi:10.3788/AOS201838.1210002

Journals >Acta Optica Sinica >Volume 38 >Issue 12 >Page 1210002 > Article

Acta Optica Sinica
Vol. 38, Issue 12, 1210002 (2018)

Sparse Representation-Based Full-Reference Quality Assessment of Distorted Satellite Stereo Images

Yiming Xiong^*, Feng Shao^*, and Xiangchao Meng

Author Affiliations

Faculty of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China

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DOI: 10.3788/AOS201838.1210002 Cite this Article Set citation alerts

Yiming Xiong, Feng Shao, Xiangchao Meng. Sparse Representation-Based Full-Reference Quality Assessment of Distorted Satellite Stereo Images[J]. Acta Optica Sinica, 2018, 38(12): 1210002 Copy Citation Text

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Fig. 1. Left images of the original satellite stereo images in the database

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Fig. 2. Block diagram of building detection

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Fig. 3. Original left image of building detection. (a) Corner detection results of original image; (b) DSM corresponding to Fig. (a); (c) building detection results

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Fig. 4. Building detection in some areas. (a) Corner detection results; (b) DSM corresponding to Fig. (a); (c) building test results

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Fig. 5. Building detection results. (a) Original image; (b) blur distortion image; (c) noise distortion image; (d) building detection of original image; (e) building detection result after blur distortion; (f) building detection result after noise distortion

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Fig. 6. Building detection results. (a) Building detection results of original image; (b) building detection results after blur distortion; (c) building detection results after noise distortion

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Fig. 7. Histogram of detection accuracy in the database

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Fig. 8. Block diagram of objective quality evaluation. (a) Feature extraction; (b) sparse representation-based similarity measure

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Fig. 9. SIFT features extraction. (a) SIFT features extraction of the original image; (b) SIFT features extraction of distorted image

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Fig. 10. Scatter plots of evaluation prediction values and detection accuracy rates obtained by different evaluation methods. (a) MS-SSIM; (b) SSIM; (c) IFC; (d) VIF; (e) model in Ref.[27]; (f) FSIM; (g) GSM; (h) model in Ref.[29]; (i) proposed method

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Criteria	$\begin{matrix} Q_{S}^{SIFT} \end{matrix}$	$\begin{matrix} Q_{O}^{SIFT} \end{matrix}$	$\begin{matrix} Q_{S}^{BRISK} \end{matrix}$	$\begin{matrix} Q_{O}^{BRISK} \end{matrix}$	Q_f
PLCC	0.8213	0.7203	0.8204	0.8469	0.9013
SROCC	0.7951	0.7048	0.8024	0.8024	0.8772
KROCC	0.5976	0.5279	0.5755	0.6065	0.7043
RMSE	6.6405	8.2312	6.7287	6.3688	5.0103

Table 1. Performance comparison of individual quality values

Model	PLCC	SROCC	KROCC	RMSE
MS-SSIM	0.8146	0.7938	0.6288	6.9891
SSIM	0.6551	0.6135	0.4311	8.8133
IFC	0.8285	0.8154	0.6705	6.6789
VIF	0.8890	0.8722	0.6839	6.3872
Model in Ref.[27]	0.7929	0.7445	0.5477	7.0585
FSIM	0.6971	0.6623	0.4735	8.2684
Model in Ref.[29]	0.6891	0.6750	0.4830	8.4530
GSM	0.6610	0.6916	0.4987	8.6132
Proposed	0.9013	0.8772	0.7043	5.0103

Table 2. Comparison of overall performance of different evaluation methods

Distortion	Model
Distortion	MS-SSIM	SSIM	IFC	VIF	Model in Ref.[27]	FSIM	Model in Ref.[29]	GSM	Proposed
Gblur	0.8624	0.6651	0.8342	0.9152	0.8374	0.6494	0.6475	0.7013	0.8901
WN	0.8617	0.5321	0.8360	0.8823	0.7632	0.5836	0.5952	0.5579	0.9272

Table 3. PLCC values of different distortion types

Distortion	Model
Distortion	MS-SSIM	SSIM	IFC	VIF	Model in Ref.[27]	FSIM	Model in Ref.[29]	GSM	Proposed
Gblur	0.9384	0.7079	0.8714	0.8629	0.7774	0.7074	0.7147	0.7384	0.8567
WN	0.8967	0.6719	0.8463	0.8513	0.7577	0.6824	0.6904	0.7013	0.9076

Table 4. SROCC values of different distortion types

Yiming Xiong, Feng Shao, Xiangchao Meng. Sparse Representation-Based Full-Reference Quality Assessment of Distorted Satellite Stereo Images[J]. Acta Optica Sinica, 2018, 38(12): 1210002

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