Objective Assessment of Stereoscopic Image Comfort Based on Convolutional Neural Network

Grade	Criteria for judging image damage	Degree of comfort
5	Almost no distortion	Excellent
4	Slightly distorted but not repugnant	Good
3	General distortion and a little repugnant	Fair
2	Obviously distorted but not disgusting	Poor
1	Serious distorted and disgusting	Bad

Table 2. Parameter setting of network model

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Table 2. Parameter setting of network model

Patch_256_net	Patch_32_net	Pca_net
Conv-20(5×5)	Conv-20(5×5)	Conv-50(3×3)
Max(3×3)	Max(2×2)	Conv-50(3×3)
Conv-100(5×5)	Conv-50(5×5)	Conv-50(3×3)
Max(4×4)	Max(2×2)	Max(3×3)
Conv-100(5×5)	-	-
Conv-100(4×4)
Max(3×3)
Conv-100(3×3)
Max(3×3)
FC-2500
FC-600
FC-5

Table 3. Recognition rates of test samples with different structures of CNN model
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Table 3. Recognition rates of test samples with different structures of CNN model
Structure of CNN model Recognition rate/%
Patch_32_net 51.67
Patch_256_net 55.00
Patch_32_256_net 66.25
PCA_net 76.75
PCA_32_net 84.50
PCA_256_net 88.25
PCA_32_256_net 94.52

Table 4. Influence of Dropout and LRN layer on PCA_32_256_net model recognition rate
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Table 4. Influence of Dropout and LRN layer on PCA_32_256_net model recognition rate
Optimization method Recognition rate /%
Dropout layer LRN layer
No Yes 93.20
Yes No 93.40
Yes Yes 94.52

Table 5. Recognition rates of test algorithms
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Table 5. Recognition rates of test algorithms
Algorithm Recognition rate /% Train time /s Test time /s
Proposed 94.52 7920.0000 0.1470
SVM^[6] 92.50 20.2700 0.0047
ELM^[7] 93.85 0.0025 0.0037

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Sumei Li, Yongli Chang, Zhicheng Duan. Objective Assessment of Stereoscopic Image Comfort Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(6): 0610003

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