Laser & Optoelectronics Progress, Volume. 58, Issue 24, 2410007(2021)
No-Reference Stereoscopic Image Quality Assessment Based on Binocular Neuron Response
Figures & Tables(10)
Table 1. Summary of extracted features
View tableTable 1. Summary of extracted features
Feature Feature description Symbolic representation DCT domain feature 10% and 100% pooled shape parameter f1 and f2 10% and 100% pooled coefficient of frequency variation f3 and f4 10% and 100% pooled energy sub-band ratio f5 and f6 10% and 100% pooled orientation feature f7 and f8 Spatial feature (α,σ2) from GGD fit of MSCN coefficients f9 and f10 (η,υ, , ) from AGGD fit of four adjacent MSCN coefficients f11-f26
Table 2. Performance comparison among different assessment metrics on LIVE-I and LIVE-II
View tableTable 2. Performance comparison among different assessment metrics on LIVE-I and LIVE-II
Method Type LIVE-I LIVE-II PLCC SROCC RMSE PLCC SROCC RMSE FI-PSNR FR 0.865 0.856 8.242 0.658 0.638 8.496 FI-SSIM FR 0.870 0.861 8.087 0.684 0.680 8.230 Method in Ref.[8] FR 0.925 0.922 - 0.759 0.745 - Method in Ref. [9] FR 0.889 0.877 7.519 0.770 0.751 7.204 Method in Ref. [10] FR 0.918 0.909 6.501 0.907 0.901 4.766 Method in Ref. [3] NR 0.917 0.911 5.864 0.737 0.701 7.665 Method in Ref. [4] NR 0.910 0.901 6.794 0.750 0.701 7.198 Method in Ref. [13] NR 0.922 0.903 6.258 0.913 0.905 4.657 Method in Ref. [15] NR 0.891 0.885 - 0.784 0.805 - Method in Ref. [22] NR 0.938 0.868 - 0.851 0.831 - Proposed method NR 0.938 0.927 5.583 0.937 0.931 3.901
Table 3. SROCC results of different assessment metrics on LIVE-I and LIVE-II for different distortion types
View tableTable 3. SROCC results of different assessment metrics on LIVE-I and LIVE-II for different distortion types
Method LIVE-I LIVE-II JPEG JP2K WN GB FF JPEG JP2K WN GB FF FI-PSNR 0.207 0.839 0.928 0.935 0.658 0.613 0.719 0.907 0.711 0.701 FI-SSIM 0.241 0.822 0.928 0.879 0.687 0.564 0.700 0.909 0.739 0.735 Method in Ref. [8] 0.615 0.875 0.943 0.938 0.781 0.720 0.848 0.846 0.801 0.851 Method in Ref. [9] 0.347 0.819 0.908 0.918 0.653 0.846 0.804 0.939 0.884 0.874 Method in Ref. [10] 0.440 0.865 0.937 0.924 0.758 0.840 0.833 0.955 0.910 0.889 Method in Ref. [3] 0.699 0.890 0.899 0.922 0.649 0.566 0.704 0.459 0.896 0.711 Method in Ref. [4] 0.570 0.812 0.940 0.878 0.784 0.605 0.695 0.440 0.860 0.683 Method in Ref. [13] 0.603 0.838 0.906 0.791 0.679 0.818 0.845 0.946 0.903 0.899 Method in Ref. [15] 0.693 - 0.899 0.853 - 0.622 - 0.803 0.713 - Method in Ref. [22] 0.633 - 0.920 0.903 - 0.788 - 0.929 0.909 - Proposed method 0.742 0.891 0.920 0.867 0.737 0.847 0.850 0.942 0.914 0.903
Table 4. Performance comparison among different assessment metrics on NBU-MDSID I and NBU-MDSID II
View tableTable 4. Performance comparison among different assessment metrics on NBU-MDSID I and NBU-MDSID II
Method Type NBU-MDSID I NBU-MDSID II PLCC SROCC RMSE PLCC SROCC RMSE Method in Ref. [8] FR 0.919 0.905 3.687 0.802 0.862 7.212 Method in Ref. [9] FR 0.856 0.834 4.943 0.820 0.780 7.110 Method in Ref. [10] FR 0.885 0.877 4.385 0.763 0.749 7.560 Method in Ref. [3] NR 0.937 0.930 3.244 0.835 0.800 6.589 Method in Ref. [4] NR 0.920 0.900 3.701 0.824 0.799 6.827 Method in Ref. [14] NR 0.934 0.920 3.348 0.791 0.763 7.180 Method in Ref. [15] NR 0.878 0.882 4.570 0.606 0.627 9.586 Method in Ref. [16] NR 0.916 0.922 3.836 0.785 0.765 7.442 Method in Ref. [30] NR 0.938 0.926 3.062 - - - Method in Ref. [22] NR 0.940 0.936 3.804 0.845 0.819 7.020 Proposed method NR 0.963 0.952 3.887 0.859 0.851 6.110
Table 5. Performance comparison among individual response and comprehensive response of five types of neurons
View tableTable 5. Performance comparison among individual response and comprehensive response of five types of neurons
Database Index Neuron response ODF TE ODF TI ODF ODD RPC TE RPC ODD All LIVE-I PLCC 0.944 0.942 0.944 0.943 0.946 0.938 SROCC 0.934 0.925 0.933 0.936 0.938 0.927 LIVE-II PLCC 0.899 0.909 0.912 0.905 0.936 0.937 SROCC 0.864 0.883 0.880 0.872 0.926 0.931 NBU-MDSID I PLCC 0.960 0.950 0.952 0.959 0.955 0.963 SROCC 0.939 0.922 0.922 0.936 0.926 0.952 NBU-MDSID II PLCC 0.807 0.702 0.729 0.810 0.748 0.859 SROCC 0.788 0.673 0.712 0.793 0.736 0.851
Table 6. SROCC results of different distortion types under five types of neuron responses
View tableTable 6. SROCC results of different distortion types under five types of neuron responses
Database Type Neuron response ODF TE ODF TI ODF ODD RPC TE RPC ODD LIVE-I JPEG 0.730 0.671 0.702 0.765 0.751 JP2K 0.886 0.892 0.874 0.895 0.881 WN 0.901 0.885 0.908 0.902 0.915 GB 0.880 0.864 0.874 0.885 0.884 FF 0.762 0.787 0.758 0.767 0.713 LIVE-II JPEG 0.719 0.781 0.722 0.716 0.798 JP2K 0.762 0.819 0.744 0.768 0.885 WN 0.948 0.917 0.947 0.945 0.943 GB 0.927 0.932 0.915 0.931 0.920 FF 0.861 0.916 0.880 0.857 0.916
Table 7. Running time of each algorithm
View tableTable 7. Running time of each algorithm
Algorithm Method in Ref.[10] Method in Ref. [3] Method in Ref. [4] Method in Ref. [13] Method in Ref. [22] Proposed method Time /s 20.799 31.295 0.542 153.356 7.760 8.703
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Mengmeng Ye, Jinbin Hu, Xuejin Wang, Feng Shao. No-Reference Stereoscopic Image Quality Assessment Based on Binocular Neuron Response[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2410007
Paper Information
Category: Image Processing
Received: Jan. 4, 2021
Accepted: Mar. 5, 2021
Published Online: Nov. 24, 2021
The Author Email: Feng Shao (shaofeng@126.com)
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