Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1957-1964.DOI: 10.11772/j.issn.1001-9081.2021040597
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Jia LI1, Yuanlin ZHENG1,2(), Kaiyang LIAO1,2, Haojie LOU1, Shiyu LI1, Zehao CHEN1
Received:
2021-04-16
Revised:
2021-07-02
Accepted:
2021-07-15
Online:
2022-06-22
Published:
2022-06-10
Contact:
Yuanlin ZHENG
About author:
LI Jia,born in 1997,M. S. candidate. Her research interests include deep learning,image processingSupported by:
李佳1, 郑元林1,2(), 廖开阳1,2, 楼豪杰1, 李世宇1, 陈泽豪1
通讯作者:
郑元林
作者简介:
李佳(1997—),女,四川广安人,硕士研究生,主要研究方向:深度学习、图像处理基金资助:
CLC Number:
Jia LI, Yuanlin ZHENG, Kaiyang LIAO, Haojie LOU, Shiyu LI, Zehao CHEN. No-reference image quality assessment algorithm based on saliency deep features[J]. Journal of Computer Applications, 2022, 42(6): 1957-1964.
李佳, 郑元林, 廖开阳, 楼豪杰, 李世宇, 陈泽豪. 基于显著性深层特征的无参考图像质量评价算法[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1957-1964.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040597
数据集 | IQA | SSIM | FSIMc | VSI | GMSD | SPSIM | LLM | 本文算法 |
---|---|---|---|---|---|---|---|---|
TID2013 | SROCC | 0.742 | 0.851 | 0.897 | 0.805 | 0.904 | 0.904 | 0.918 |
PLCC | 0.789 | 0.877 | 0.900 | 0.854 | 0.909 | 0.907 | 0.932 | |
KROCC | 0.559 | 0.667 | 0.718 | 0.633 | 0.725 | 0.721 | 0.758 | |
RMSE | 0.761 | 0.596 | 0.540 | 0.644 | 0.517 | 0.528 | 0.461 | |
TID2008 | SROCC | 0.775 | 0.884 | 0.898 | 0.891 | 0.910 | 0.908 | 0.927 |
PLCC | 0.773 | 0.876 | 0.876 | 0.872 | 0.893 | 0.897 | 0.933 | |
KROCC | 0.577 | 0.699 | 0.712 | 0.709 | 0.730 | 0.737 | 0.773 | |
RMSE | 0.851 | 0.647 | 0.647 | 0.657 | 0.605 | 0.598 | 0.436 | |
LIVE | SROCC | 0.948 | 0.965 | 0.952 | 0.960 | 0.962 | 0.961 | 0.982 |
PLCC | 0.945 | 0.961 | 0.943 | 0.960 | 0.960 | 0.958 | 0.983 | |
KROCC | 0.796 | 0.836 | 0.806 | 0.827 | 0.827 | 0.823 | 0.831 | |
RMSE | 8.946 | 7.530 | 8.682 | 7.621 | 7.629 | 7.768 | 7.343 | |
CSIQ | SROCC | 0.876 | 0.931 | 0.942 | 0.957 | 0.944 | 0.905 | 0.963 |
PLCC | 0.861 | 0.919 | 0.928 | 0.954 | 0.934 | 0.900 | 0.944 | |
KROCC | 0.691 | 0.769 | 0.786 | 0.813 | 0.788 | 0.724 | 0.828 | |
RMSE | 0.133 | 0.103 | 0.098 | 0.079 | 0.093 | 0.123 | 0.077 |
Tab. 1 Performance comparison of the proposed algorithm and mainstream IQA algorithm on experimental dataset
数据集 | IQA | SSIM | FSIMc | VSI | GMSD | SPSIM | LLM | 本文算法 |
---|---|---|---|---|---|---|---|---|
TID2013 | SROCC | 0.742 | 0.851 | 0.897 | 0.805 | 0.904 | 0.904 | 0.918 |
PLCC | 0.789 | 0.877 | 0.900 | 0.854 | 0.909 | 0.907 | 0.932 | |
KROCC | 0.559 | 0.667 | 0.718 | 0.633 | 0.725 | 0.721 | 0.758 | |
RMSE | 0.761 | 0.596 | 0.540 | 0.644 | 0.517 | 0.528 | 0.461 | |
TID2008 | SROCC | 0.775 | 0.884 | 0.898 | 0.891 | 0.910 | 0.908 | 0.927 |
PLCC | 0.773 | 0.876 | 0.876 | 0.872 | 0.893 | 0.897 | 0.933 | |
KROCC | 0.577 | 0.699 | 0.712 | 0.709 | 0.730 | 0.737 | 0.773 | |
RMSE | 0.851 | 0.647 | 0.647 | 0.657 | 0.605 | 0.598 | 0.436 | |
LIVE | SROCC | 0.948 | 0.965 | 0.952 | 0.960 | 0.962 | 0.961 | 0.982 |
PLCC | 0.945 | 0.961 | 0.943 | 0.960 | 0.960 | 0.958 | 0.983 | |
KROCC | 0.796 | 0.836 | 0.806 | 0.827 | 0.827 | 0.823 | 0.831 | |
RMSE | 8.946 | 7.530 | 8.682 | 7.621 | 7.629 | 7.768 | 7.343 | |
CSIQ | SROCC | 0.876 | 0.931 | 0.942 | 0.957 | 0.944 | 0.905 | 0.963 |
PLCC | 0.861 | 0.919 | 0.928 | 0.954 | 0.934 | 0.900 | 0.944 | |
KROCC | 0.691 | 0.769 | 0.786 | 0.813 | 0.788 | 0.724 | 0.828 | |
RMSE | 0.133 | 0.103 | 0.098 | 0.079 | 0.093 | 0.123 | 0.077 |
算法 | LIVE | TID2013 | ||
---|---|---|---|---|
SROCC | PLCC | SROCC | PLCC | |
DIQaM-FR[ | 0.966 | 0.977 | 0.859 | 0.880 |
DIIVINE[ | 0.925 | 0.923 | 0.549 | 0.654 |
CORNIA[ | 0.942 | 0.935 | 0.549 | 0.613 |
BIQI[ | 0.841 | 0.843 | 0.860 | 0.870 |
DIQaM-NR[ | 0.960 | 0.972 | 0.835 | 0.855 |
hyperIQA[ | 0.968 | 0.966 | — | — |
H-IQA[ | 0.982 | 0.982 | 0.871 | — |
RankIQA[ | 0.981 | 0.982 | 0.780 | — |
本文算法 | 0.982 | 0.983 | 0.918 | 0.932 |
Tab. 2 Performance comparison between different algorithms on LIVE and TID2013 databases
算法 | LIVE | TID2013 | ||
---|---|---|---|---|
SROCC | PLCC | SROCC | PLCC | |
DIQaM-FR[ | 0.966 | 0.977 | 0.859 | 0.880 |
DIIVINE[ | 0.925 | 0.923 | 0.549 | 0.654 |
CORNIA[ | 0.942 | 0.935 | 0.549 | 0.613 |
BIQI[ | 0.841 | 0.843 | 0.860 | 0.870 |
DIQaM-NR[ | 0.960 | 0.972 | 0.835 | 0.855 |
hyperIQA[ | 0.968 | 0.966 | — | — |
H-IQA[ | 0.982 | 0.982 | 0.871 | — |
RankIQA[ | 0.981 | 0.982 | 0.780 | — |
本文算法 | 0.982 | 0.983 | 0.918 | 0.932 |
IQA | TID2013 | LIVE | ||
---|---|---|---|---|
CSIQ | LIVE | CSIQ | TID2013 | |
BRISQUE[ | 0.639 | 0.681 | 0.577 | 0.367 |
BLIINDS-II[ | 0.456 | 0.836 | 0.577 | 0.393 |
DIIVINE[ | 0.146 | 0.687 | 0.596 | 0.355 |
CORNIA[ | 0.656 | — | 0.663 | 0.429 |
DIQaM-NR[ | 0.717 | 0.982 | 0.681 | 0.392 |
本文算法 | 0.761 | 0.953 | 0.693 | 0.482 |
Tab. 3 SROCC in cross-dataset test
IQA | TID2013 | LIVE | ||
---|---|---|---|---|
CSIQ | LIVE | CSIQ | TID2013 | |
BRISQUE[ | 0.639 | 0.681 | 0.577 | 0.367 |
BLIINDS-II[ | 0.456 | 0.836 | 0.577 | 0.393 |
DIIVINE[ | 0.146 | 0.687 | 0.596 | 0.355 |
CORNIA[ | 0.656 | — | 0.663 | 0.429 |
DIQaM-NR[ | 0.717 | 0.982 | 0.681 | 0.392 |
本文算法 | 0.761 | 0.953 | 0.693 | 0.482 |
失真类型 | BRISQUE | DIIVINE | RankIQA | H-IQA | 本文算法 |
---|---|---|---|---|---|
均值 | 0.551 | 0.506 | 0.691 | 0.681 | 0.810 |
AGN | 0.706 | 0.855 | 0.667 | 0.923 | 0.875 |
ANC | 0.523 | 0.712 | 0.620 | 0.880 | 0.853 |
SCN | 0.776 | 0.463 | 0.821 | 0.945 | 0.800 |
MN | 0.295 | 0.675 | 0.365 | 0.673 | 0.782 |
HFN | 0.836 | 0.878 | 0.760 | 0.955 | 0.915 |
IN6 | 0.802 | 0.806 | 0.736 | 0.810 | 0.735 |
QN | 0.682 | 0.165 | 0.783 | 0.855 | 0.937 |
GB | 0.861 | 0.934 | 0.809 | 0.832 | 0.960 |
DEN | 0.500 | 0.723 | 0.767 | 0.957 | 0.963 |
JPEG | 0.790 | 0.629 | 0.866 | 0.914 | 0.902 |
JP2K | 0.779 | 0.853 | 0.878 | 0.624 | 0.949 |
JGTE | 0.254 | 0.239 | 0.704 | 0.460 | 0.869 |
J2TE | 0.723 | 0.061 | 0.810 | 0.782 | 0.906 |
NEPN | 0.213 | 0.060 | 0.512 | 0.664 | 0.874 |
Block | 0.197 | 0.093 | 0.622 | 0.122 | 0.588 |
MS | 0.217 | 0.010 | 0.268 | 0.182 | 0.303 |
CTC | 0.079 | 0.460 | 0.613 | 0.376 | 0.627 |
CCS | 0.113 | 0.068 | 0.662 | 0.156 | 0.744 |
MGN | 0.674 | 0.787 | 0.619 | 0.850 | 0.806 |
CN | 0.198 | 0.116 | 0.644 | 0.614 | 0.661 |
LCNI | 0.627 | 0.633 | 0.800 | 0.852 | 0.816 |
ICQD | 0.849 | 0.436 | 0.779 | 0.911 | 0.837 |
CHA | 0.724 | 0.661 | 0.629 | 0.381 | 0.882 |
SSR | 0.811 | 0.833 | 0.859 | 0.616 | 0.868 |
Tab. 4 Performance (SROCC) comparison on TID2013 dataset for single distortion types
失真类型 | BRISQUE | DIIVINE | RankIQA | H-IQA | 本文算法 |
---|---|---|---|---|---|
均值 | 0.551 | 0.506 | 0.691 | 0.681 | 0.810 |
AGN | 0.706 | 0.855 | 0.667 | 0.923 | 0.875 |
ANC | 0.523 | 0.712 | 0.620 | 0.880 | 0.853 |
SCN | 0.776 | 0.463 | 0.821 | 0.945 | 0.800 |
MN | 0.295 | 0.675 | 0.365 | 0.673 | 0.782 |
HFN | 0.836 | 0.878 | 0.760 | 0.955 | 0.915 |
IN6 | 0.802 | 0.806 | 0.736 | 0.810 | 0.735 |
QN | 0.682 | 0.165 | 0.783 | 0.855 | 0.937 |
GB | 0.861 | 0.934 | 0.809 | 0.832 | 0.960 |
DEN | 0.500 | 0.723 | 0.767 | 0.957 | 0.963 |
JPEG | 0.790 | 0.629 | 0.866 | 0.914 | 0.902 |
JP2K | 0.779 | 0.853 | 0.878 | 0.624 | 0.949 |
JGTE | 0.254 | 0.239 | 0.704 | 0.460 | 0.869 |
J2TE | 0.723 | 0.061 | 0.810 | 0.782 | 0.906 |
NEPN | 0.213 | 0.060 | 0.512 | 0.664 | 0.874 |
Block | 0.197 | 0.093 | 0.622 | 0.122 | 0.588 |
MS | 0.217 | 0.010 | 0.268 | 0.182 | 0.303 |
CTC | 0.079 | 0.460 | 0.613 | 0.376 | 0.627 |
CCS | 0.113 | 0.068 | 0.662 | 0.156 | 0.744 |
MGN | 0.674 | 0.787 | 0.619 | 0.850 | 0.806 |
CN | 0.198 | 0.116 | 0.644 | 0.614 | 0.661 |
LCNI | 0.627 | 0.633 | 0.800 | 0.852 | 0.816 |
ICQD | 0.849 | 0.436 | 0.779 | 0.911 | 0.837 |
CHA | 0.724 | 0.661 | 0.629 | 0.381 | 0.882 |
SSR | 0.811 | 0.833 | 0.859 | 0.616 | 0.868 |
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