基于非局部梯度的图像质量评价算法
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摘要
图像质量评价研究的目标在于模拟人类视觉系统对图像质量的感知过程,构建与主观评价结果尽可能一致的客观评价算法。现有的很多算法都是基于局部结构相似设计的,但人对图像的主观感知是高级的、语义的过程,而语义信息本质上是非局部的,因此图像质量评价应该考虑图像的非局部信息。该文突破了经典的基于局部信息的算法框架,提出一种基于非局部信息的框架,并在此框架内构建了一种基于非局部梯度的图像质量评价算法,该算法通过度量参考图像与失真图像的非局部梯度之间的相似性来预测图像质量。在公开测试数据库TID2008, LIVE, CSIQ上的数值实验结果表明,该算法能获得较好的评价效果。
The goal of Image Quality Assessment(IQA) research is to simulate the Human Visual System's(HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.
The goal of Image Quality Assessment(IQA) research is to simulate the Human Visual System's(HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.
引文
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[18]DING Li,HUANG Hua,and ZANG Yu.Image quality assessment using directional anisotropy structure measurement[J].IEEE Transactions on Image Processing,2017,26(4):1799-1809.doi:10.1109/TIP.2017.2665972.
[19]张帆,张偌雅,李珍珍.基于对称相位一致性的图像质量评价方法[J].激光与光电子学进展,2017,54(10):194-202.doi:10.3788/LOP54.101003.ZHANG Fan,ZHANG Ruoya,and LI Zhenzhen.Image quality assessment based on symmetry phase congruency[J].Laser&Optoelectronics Progress,2017,54(10):194-202.doi:10.3788/LOP54.101003.
[20]PONOMARENKO N,LUKIN V,ZELENSKY A,et al.TID2008:A database for evaluation of full-reference visual quality assessment metrics[OL].http://www.ponomarenko.info/papers/mre2009tid.pdf.2016.10.
[21]LARSON EC and CHANDLER D.Categorical subjective image quality(CSIQ)database[OL].http://vision.okstate.edu/csiq,2016.10.
[22]SHEIKH H R,WANG Zhou,BOVIK A C,et al.Image and video quality assessment research at LIVE[OL].http://live.ece.utexas.edu/rese-arch/quality/.2016.10.
[2]WANG Hanli,FU Jie,LIN Weisi,et al.Image quality assessment based on local linear information and distortionspecific compensation[J].IEEE Transactions on Image Processing,2017,26(2):915-926.doi:10.1109/TIP.2016.2639451.
[3]DI E C and JACOVITTI G.A detail based method for linear full reference image quality prediction[J].IEEETransactions on Image Processing,2017,27(1):179-192.doi:10.1109/TIP.2017.2757139.
[4]CHANDLER D M and HEMAMI S S.VSNR:A waveletbased visual signal-to-noise ratio for natural images[J].IEEE Transactions on Image Processing,2007,16(9):2284-2298.doi:10.1109/TIP.2007.901820.
[5]褚江,陈强,杨曦晨.全参考图像质量评价综述[J].计算机应用研究,2014,31(1):13-22.doi:10.3969/j.issn.1001-3695.2014.01.003.CHU Jiang,CHEN Qiang,and YANG Xichen.Review on full reference image quality assessment algorithms[J].Application Research of Computers,2014,31(1):13-22.doi:10.3969/j.issn.1001-3695.2014.01.003.
[6]WANG Zhou and BOVIK A C.Mean squared error:love it or leave it?A new look at signal fidelity measures[J].IEEESignal Processing Magazine,2009,26(1):98-117.doi:10.1109/MSP.2008.930649.
[7]HUYNH-THU Q and GHANBARI M.Scope of validity of PSNR in image/video quality assessment[J].Electronics Letters,2008,44(13):800-801.doi:10.1049/el:20080522.
[8]WANG Zhou,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.doi:10.1109/TIP.2003.819861.
[9]WANG Zhou,SIMONCELLI E P,and BOVIK A C.Multiscale structural similarity for image quality assessment[C].Proceedings of 37th IEEE Asilomar Conference on Signals,Systems and Computers,Pacific Grove,USA,2003:1398-1402.
[10]LI Chaofeng and BOVIK A C.Three-component weighted structural similarity index[C].SPIE Conference on Image Quality and System Performance,San Jose,USA,2009,7242:72420Q-72420Q-9.
[11]WANG Zhou and LI Qiang.Information content weighting for perceptual image quality assessment[J].IEEETransactions on Image Processing,2011,20(5):1185-1198.doi:10.1109/TIP.2010.2092435.
[12]ZHANG Lin,ZHANG Lei,MOU Xuanqin,et al.FSIM:Afeature similarity index for image quality assessment[J].IEEE Transactions on Image Processing,2011,20(8):2378-2386.doi:10.1109/TIP.2011.2109730.
[13]LIU Anmin,LIN Weisi,and NARWARIA M.Image quality assessment based on gradient similarity[J].IEEETransactions on Image Processing,2012,21(4):1500-1512.doi:10.1109/TIP.2011.2175935.
[14]XUE Wufeng,ZHANG Lei,MOU Xuanqin,et al.Gradient magnitude similarity deviation:A highly efficient perceptual image quality index[J].IEEE Transactions on Image Processing,2014,23(2):684-695.doi:10.1109/TIP.2013.2293423.
[15]ZHANG Xuande,FENG Xiangchu,WANG Weiwei,et al.Edge strength similarity for image quality assessment[J].IEEE Signal Processing Letters,2013,20(4):319-322.doi:10.1109/LSP.2013.2244081.
[16]WANG Tonghan,JIA Huizhen,and SHU Huazhong.Fullreference image quality assessment algorithm based on gradient magnitude and histogram of oriented gradient[J].Journal of Southeast University,2018,48(2):276-281.doi:10.3969/j.issn.1001-0505.2018.02.014.
[17]NI Zhangkai,MA Lin,ZENG Huanqiang,et al.Gradient direction for screen content image quality assessment[J].IEEE Signal Processing Letters,2016,23(10):1394-1398.doi:10.1109/LSP.2016.2599294.
[18]DING Li,HUANG Hua,and ZANG Yu.Image quality assessment using directional anisotropy structure measurement[J].IEEE Transactions on Image Processing,2017,26(4):1799-1809.doi:10.1109/TIP.2017.2665972.
[19]张帆,张偌雅,李珍珍.基于对称相位一致性的图像质量评价方法[J].激光与光电子学进展,2017,54(10):194-202.doi:10.3788/LOP54.101003.ZHANG Fan,ZHANG Ruoya,and LI Zhenzhen.Image quality assessment based on symmetry phase congruency[J].Laser&Optoelectronics Progress,2017,54(10):194-202.doi:10.3788/LOP54.101003.
[20]PONOMARENKO N,LUKIN V,ZELENSKY A,et al.TID2008:A database for evaluation of full-reference visual quality assessment metrics[OL].http://www.ponomarenko.info/papers/mre2009tid.pdf.2016.10.
[21]LARSON EC and CHANDLER D.Categorical subjective image quality(CSIQ)database[OL].http://vision.okstate.edu/csiq,2016.10.
[22]SHEIKH H R,WANG Zhou,BOVIK A C,et al.Image and video quality assessment research at LIVE[OL].http://live.ece.utexas.edu/rese-arch/quality/.2016.10.
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