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Cited by 1,697Image and Video Quality AssessmentImage Retrieval and Classification TechniquesDigital Media Forensic Detection

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

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Xue, Wufeng, Zhang, Lei, Mou, Xuanqin, Bovik, Alan C.·2014·IEEE Transactions on Image Processing
DOI 10.1109/tip.2013.2293423
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Abstract

It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.

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  1. [1][1] Z. Wang, 'Applications of objective image quality assessment methods,' IEEE Signal Processing Magazine, vol. 28, no. 6, pp. 137 -142, Nov. 2011. -
  2. [2][2] B. Girod, 'What's wrong with mean-squared error?' Digital Images and Human Vision . MIT press, 1993. -
  3. [3][3] Z. Wang, and A. C. Bovik, 'Modern Image Quality Assessment,' Synthesis Lectures on Image, Video, and Multimedia Processing , vol. 2, no.1, pp. 1-156, 2006. -
  4. [4][4] D. O. Kim, H. S. Han, and R. H. Park, 'Gradient information-based image quality metric,' IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 930-936, 2010. -
  5. [5][5] G. Q. Cheng, J. C. Huang, C. Zhu, Z. Liu and L. Z. Cheng, 'Perceptual image quality assessment using a geometric structural distortion model,' 17 th IEEE International Conference on Image Processing , 2010. -
  6. [6][6] G. H. Chen, C. L. Yang and S. L. Xie, 'Gradient-based structural similarity for image quality assessment,' 13 th IEEE International Conference on Image Processing, 2006. -
  7. [7][7] Lin Zhang, Lei Zhang, X. Mou and D. Zhang, 'FSIM: A Feature Similarity Index for Image Quality Assessment,' IEEE Trans. Image Process. , vol. 20, no. 8, pp. 2378-2386, Aug. 2011. -
  8. [9][9] Z. Wang and X. Shang, 'Spatial pooling strategies for perceptual image quality assessment,' IEEE Int. Conf. Image Process. , Sep. 2006, pp. 2945-2948. -
  9. [10][10] A.K. Moorthy and A.C. Bovik, 'Visual importance pooling for image quality assessment,' IEEE J. Special Topics Signal Process , vol. 3, pp. 193-201, April 2009. -
  10. [13][13] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, F. Battisti, 'TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics,' Advances of Modern Radio electronics, Vol. 10, pp. 30-45, 2009. -
  11. [14][14] Lin Zhang, Lei Zhang, X. Mou and D. Zhang, 'A Comprehensive Evaluation of Full Reference Image Quality Assessment Algorithms,' IEEE Int. Conf. Image Process. 2012. -
  12. [15][15] A. Liu, W. Lin, and Manish Narwaria, 'Image Quality Assessment Based on Gradient Similarity,' IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1500-1512, 2012. -
  13. [16][16] Z. Wang, and Q. Li, 'Information content weighting for perceptual image quality assessment,' IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1185-1198, 2011. -
  14. [18][18] M. Zhang, X. Mou, and L. Zhang, 'Non-Shift Edge Based Ratio (NSER): An Image Quality Assessment Metric Based on Early Vision Features,' IEEE Signal Processing Letters, vol. 18, no .5, pp. 315-318, 2011. -
  15. [19][19] C. F. Li, and A. C. Bovik, 'Content-partitioned structural similarity index for image quality assessment,' Signal Processing: Image Communication , vol. 25, no. 7, pp. 517-526, 2010. -
  16. [20][20] Y. Tong, Hubert Konik, F. A. Cheikh and Alain Tremeau, 'Full reference image quality assessment based on saliency map analysis,' Journal of Imaging Science , vol. 54, no. 3, pp. 30503-30503, 2010. -
  17. [22][22] H.R. Sheikh, A.C. Bovik and G. de Veciana, 'An information fidelity criterion for image quality assessment using natural scene statistics,' IEEE Transactions on Image Processing , vol.14, no.12pp. 2117- 2128, Dec. 2005. -
  18. [23][23] H.R. Sheikh, and A.C. Bovik, 'Image information and visual quality,' IEEE Transactions on Image Processing , vol.15, no.2, pp. 430- 444, Feb. 2006. -
  19. [24][24] W. Xue, and X. Mou, 'An image quality assessment metric based on Non-shift Edge,' 18th IEEE International Conference on Image Processing (ICIP) , 2011. -
  20. [25][25] Z. Wang and A. C. Bovik, 'Mean squared error: love it or leave it? - A new look at signal fidelity measures,' IEEE Signal Processing Magazine , vol. 26, no. 1, pp. 98-117, Jan. 2009. -
  21. [26][26] A.L. Neuenschwander, M.M. Crawford, L.A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton and R. Heinrichs, 'Classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,' Proc. SPIE 7684, 768408 (2010). -
  22. [27][27] S. A. Coleman, B. W. Scotney, and S. Suganthan, 'Multi-scale edge detection on range and intensity images,' Pattern Recognition , vol. 44, no. 4, pp. 821-838, 2011. -
  23. [28][28] Nezhadarya Ehsan, and Rabab K. Ward, 'An efficient method for robust gradient estimation of RGB color images,' 16th IEEE International Conference on Image Processing (ICIP) , 2009. -
  24. [29][29] J. Park, K. Seshadrinathan, S. Lee and A.C. Bovik, 'VQpooling: Video quality pooling adaptive to perceptual distortion severity,' IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 610-620, February 2013. -
  25. [30][30] W. Lin, C.-C. Jay Kuo, 'Perceptual visual quality metrics: A survey,' Journal of Visual Communication and Image Representation , vol. 22, no. 4, pp. 297-312, May 2011. -
  26. [31][31] Ninassi, Alexandre, et al., 'Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric,' IEEE International Conference on Image Processing, vol. 2, 2007. -
  27. [32][32] J. Ross and H. D. Speed, 'Contrast adaptation and contrast masking in human vision,' Proceedings. Biological Sciences / The Royal Society , vol. 246, no. 1315, pp. 61-9, Oct. 1991. -
  28. [33][33] S. J. Daly, 'Application of a noise-adaptive contrast sensitivity function to image data compression,' Optical Engineering , vol. 29, pp. 977-987, 1990. -
  29. [34][34] Lubin, Jeffrey. 'A human vision system model for objective picture quality measurements,' International Broadcasting Conference (IBC) , January 1997 p. 498 - 503. -
  30. [35][35] Carlos M. Jarque, Anil K. Bera, 'Efficient tests for normality, homoscedasticity and serial independence of regression residuals,' Economics Letters 6 (3): 255-259, 1980. -
  31. [36][36] Chao Wang, Xuanqin Mou, Wei Hong, and Lei Zhang, 'Block-layer bit allocation for quality constrained video encoding based on constant perceptual quality,' Proceedings of SPIE Vol. 8666, 86660J (2013). -
  32. [37][37] Tao-Sheng Ou, Yi-Hsin Huang, and Homer H. Chen, 'SSIM-based perceptual rate control for video coding," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 682-691, 2011. -
  33. [38][38] Shiqi Wang, Abdul Rehman, Z. Wang, Siwei Ma, and Wen Gao, 'SSIM-motivated rate-distortion optimization for video coding,' IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 4 (2012): 516-529. -
  34. [41][41] A. K. Moorthy, L. K. Choi, A. C. Bovik and G. deVeciana, 'Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies,' IEEE Journal of Selected Topics in Signal Processing , vol. 6, no. 6, pp. 652-671, October 2012. -
  35. [42][42] Ming-Jun Chen, and A. C. Bovik, 'Fast structural similarity index algorithm,' Journal of Real-Time Image Processing , vol. 6, no. 4 (2011): 281-287. -

Cited by

Sorted by citations

Authors
Xue, W., Zhang, L., Mou, X., & Bovik, A. C.
Affiliations
  • Hong Kong Polytechnic University (HK)
  • Xi'an Jiaotong University (CN)
  • The University of Texas at Austin (US)
ORCID
Venue
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2014-02
Volume
23
Issue
2
Pages
684-695
ISSN
  • 1057-7149 (ISSN-L)
  • 1941-0042
DOI
10.1109/tip.2013.2293423
OpenAlex
W1964859077
Cited by
1,697
References
3
Citations by year
2026: 136 · 2025: 158 · 2024: 176 · 2023: 184 · 2022: 172 · 2021: 159 · 2020: 139 · 2019: 170 · 2018: 125 · 2017: 99 · 2016: 88 · 2015: 66 · 2014: 25
FWCI
82.80
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Image Processing (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (61172163, 90920003)
  • Hong Kong Polytechnic University (5315/12E)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Keywords
perceptual image quality assessment, image quality index, computational efficiency, pooling strategies, image distortion, image restoration, image compression

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