Image Quality Assessment Algorithm Based on Non-local Gradient

Minjuan GAO; Hongshe DANG; Lili WEI; Xuande ZHANG

doi:10.11999/JEIT180597

Volume 41 Issue 5

Apr. 2019

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Minjuan GAO, Hongshe DANG, Lili WEI, Xuande ZHANG. Image Quality Assessment Algorithm Based on Non-local Gradient[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1122-1129. doi: 10.11999/JEIT180597

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Image Quality Assessment Algorithm Based on Non-local Gradient

doi: 10.11999/JEIT180597

1.
College of Electrical and Information Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
2.
School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China

Funds: The National Natural Science Foundation of China (61871260, 61603234, 61362029, 61461043)

Received Date: 2018-06-19
Rev Recd Date: 2018-12-18

Available Online: 2018-12-26

Publish Date: 2019-05-01

Abstract

Abstract

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.
- Image Quality Assessment (IQA),
- Human Visual System (HVS),
- Non-local gradient

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References(22)

References

BAE S H and KIM M. A novel image quality assessment with globally and locally consilient visual quality perception[J]. IEEE Transactions on Image Processing, 2016, 25(5): 2392–2406. doi: 10.1109/TIP.2016.2545863

WANG Hanli, FU Jie, LIN Weisi, et al. Image quality assessment based on local linear information and distortion-specific compensation[J]. IEEE Transactions on Image Processing, 2017, 26(2): 915–926. doi: 10.1109/TIP.2016.2639451

DI E C and JACOVITTI G. A detail based method for linear full reference image quality prediction[J]. IEEE Transactions on Image Processing, 2017, 27(1): 179–192. doi: 10.1109/TIP.2017.2757139

CHANDLER D M and HEMAMI S S. VSNR: A wavelet-based 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

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

WANG Zhou and BOVIK A C. Mean squared error: love it or leave it? A new look at signal fidelity measures[J]. IEEE Signal Processing Magazine, 2009, 26(1): 98–117. doi: 10.1109/MSP.2008.930649

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

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

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.

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.

WANG Zhou and LI Qiang. Information content weighting for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1185–1198. doi: 10.1109/TIP.2010.2092435

ZHANG Lin, ZHANG Lei, MOU Xuanqin, et al. FSIM: A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378–2386. doi: 10.1109/TIP.2011.2109730

LIU Anmin, LIN Weisi, and NARWARIA M. Image quality assessment based on gradient similarity[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1500–1512. doi: 10.1109/TIP.2011.2175935

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

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

WANG Tonghan, JIA Huizhen, and SHU Huazhong. Full-reference 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

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

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

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

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.

LARSON EC and CHANDLER D. Categorical subjective image quality (CSIQ) database[OL]. http://vision.okstate.edu/csiq, 2016.10.

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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