基于特征融合的无参考屏幕图像质量评价
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摘要
考虑到人类视觉系统对图像边缘和局部纹理信息较为敏感,本文提出一种基于特征融合的无参考屏幕图像质量评价方法。所提方法通过梯度方向直方图和局部二值模式分别提取屏幕图像的边缘和局部纹理信息,接着通过特征融合过程得到一个更加能够描述屏幕失真的融合特征,最后采用支持向量回归训练得到屏幕图像融合特征向量与主观质量分数的质量评价映射模型。实验结果显示,与现有的图像质量评价方法相比,本文所提算法能够更好地反映出人类视觉系统对屏幕图像的主观感知度。
Considering the human visual system is more sensitive to the edge and local texture information, this paper presents a feature fusion based no reference quality assessment model for screen content image(SCI). In the proposed method, the Histogram of Oriented Gradient and Local Binary Pattern are exploited to describe the edge and local texture information of the SCI. A feature fusion process is subsequently conducted to obtain a feature to better reflect the distortion of SCI. Finally, the support vector regression is applied to obtain the quality assessment mapping model from the above fused feature to subjective rating. Experimental results show that the proposed method is able to better reflect the human perception on SCI, compared with multiple state-of-the-art image quality assessment methods.
Considering the human visual system is more sensitive to the edge and local texture information, this paper presents a feature fusion based no reference quality assessment model for screen content image(SCI). In the proposed method, the Histogram of Oriented Gradient and Local Binary Pattern are exploited to describe the edge and local texture information of the SCI. A feature fusion process is subsequently conducted to obtain a feature to better reflect the distortion of SCI. Finally, the support vector regression is applied to obtain the quality assessment mapping model from the above fused feature to subjective rating. Experimental results show that the proposed method is able to better reflect the human perception on SCI, compared with multiple state-of-the-art image quality assessment methods.
引文
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[2] Ni Zhangkai,Ma Lin,Zeng Huanqiang,et al.A Gabor Feature-Based Quality Assessment Model for the Screen Content Images[J].IEEE Transactions on Image Processing,2018,27(9):4516- 4528.
[3] 符颖,曾焕强,倪张凯,等.采用边缘信息的屏幕图像质量评价 [J].信号处理,2017,33(4):540-545.Fu Ying,Zeng Huanqiang,Ni Zhangkai,et al.Screen Content Image Quality Assessment Using Edge Information [J].Journal of Signal Processing,2017,33(4):540-545.(in Chinese)
[4] Wang S,Gu K,Zhang X,et al.Reduced-reference quality assessment of screen content images[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,28(1):1-14.
[5] Fang Y,Ma K,Wang Z,et al.No-reference quality assessment of contrast-distorted images based on natural scene statistics[J].IEEE Signal Processing Letters,2015,22(7):838- 842.
[6] Gu K,Zhai G,Lin W,et al.No-reference image sharpness assessment in autoregressive parameter space[J].IEEE Transactions on Image Processing,2015,24(10):3218-3231.
[7] Zhang L,Zhang L,Bovik A C.A feature-enriched completely blind image quality evaluator[J].IEEE Transactions on Image Processing,2015,24(8):2579-2591.
[8] Gu K,Zhou J,Qiao J F,et al.No-reference quality assessment of screen content pictures[J].IEEE Transactions on Image Processing,2017,26(8):4005- 4018.
[9] Fang Y,Yan J,Li L,et al.No reference quality assessment for screen content images with both local and global feature representation[J].IEEE Transactions on Image Processing,2018,27(4):1600-1610.
[10] Shao F,Gao Y,Li F,et al.Toward a blind quality predictor for screen content images[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,48(9):1521-1530.
[11] Cai L,Zhu J,Zeng H,et al.Hog-assisted deep feature learning for pedestrian gender recognition[J].Journal of the Franklin Institute,2018,355(4):1991-2008.
[12] Zeng H,Chen J,Cui X,et al.Quad binary pattern and its application in mean-shift tracking[J].Neurocomputing,2016,217:3-10.
[13] Alam S,Kang M,Pyun J Y,et al.Performance of classification based on PCA,linear SVM,and Multi-kernel SVM[C]//2016 Eighth International Conference on Ubiquitous and Future Networks(ICUFN).IEEE,2016:987-989.
[14] Yang H,Fang Y,Lin W,et al.Subjective quality assessment of screen content images[C]//2014 Sixth International Workshop on Quality of Multimedia Experience(QoMEX).IEEE,2014:257-262.
[15] Wang S,Gu K,Zhang X,et al.Subjective and objective quality assessment of compressed screen content images[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2016,6(4):532-543.
[16] Gu K,Zhai G,Lin W,et al.Learning a blind quality evaluation engine of screen content images[J].Neurocomputing,2016,196:140-149.
[17] Li Q,Lin W,Fang Y.No-reference quality assessment for multiply-distorted images in gradient domain[J].IEEE Signal Processing Letters,2016,23(4):541-545.
[18] Mittal A,Soundararajan R,Bovik A C.Making a “completely blind” image quality analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212.
[19] 邢露,曾焕强,倪张凯,等.针对对比度变化的图像质量评价方法 [J].信号处理,2017,33(3):319-323.Xing Lu,Zeng Huanqiang,Ni Zhangkai,et al.Contrast-Changed Image Quality Assessment Method [J].Journal of Signal Processing,2017,33(3):319-323.(in Chinese)
[20] Xue W,Zhang L,Mou X,et al.Gradient magnitude similarity deviation:A highly efficient perceptual image quality index[J].IEEE Transactions on Image Processing,2014,23(2):684- 695.