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Reduced-reference image quality assessment through SIFT intensity ratio

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Abstract

Scale invariant feature transform (SIFT) points are scale-space extreme points, representing local minutiae features in the Gaussian scale space. SIFT intensity ratio (SIR), as a novel reduced-reference metric, is feasible to assess various common distortions without the prior knowledge of distortion types. It describes relative changes in the number of SIFT points between a test image and its corresponding reference image. SIFT points in the metric are detected in the first octave of the difference-of-Gaussian scale space under certain preprocessings: neighborhood enhancement through a Laplacian operator to sharpen isolated points and thin edges, reducing false SIFT points; double-size image magnification through linear interpolation to amplify distortion effects, improving its sensitivity to image distortions. Experimental results demonstrate that SIR is superior to existing classic reduced-reference metrics, and can be used to assess different distortions.

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References

  1. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  2. Aja-Fernández S, Estépar RSJ, Alberola-López C, Westin C-F (2006) Image quality assessment based on local variance. In: Proceeding of the 28th IEEE EMBS Annual international conference. New York, IEEE, pp 4815–4818

  3. Li C, Bovik AC (2010) Content-partitioned structural similarity index for image quality assessment. Signal Process Image Commun 25(7):517–526

    Article  Google Scholar 

  4. Wu HR, Yuen M (1977) A generalized block-edge impairment metric for video coding. IEEE Signal Process Lett 4(11):317–320

    Article  Google Scholar 

  5. Lu W, Zeng K, Tao D, Yuan Y, Gao X (2010) No-reference image quality assessment in contourlet domain. Neurocomputing 73:784–794

    Article  Google Scholar 

  6. Zhang J, Le TM, Ong SH, Nguyen TQ (2011) No-reference image quality assessment using structural activity. Signal Process 91:2575–2588

    Article  Google Scholar 

  7. Kusuma TM, Zepernick HJ (2003) A reduced-reference perceptual quality metric for in-service image quality assessment. In: Proceeding of joint first workshop on IEEE Mobile future and symposium on trends in communications. Bratislava, Slovakia IEEE:71–74

  8. Wang Z, Simoncelli EP (2005) Reduced-reference image quality assessment image statistic model. Proc SPIE Hum Vis Electron Imaging 5666:149–159

    Google Scholar 

  9. Decherchi S, Gastaldo P, Zunino R, Cambria E, Redi J (2012) Circular-ELM for the reduced-reference assessment of perceived image quality. Neurocomputing 102:78–90

    Article  Google Scholar 

  10. Yang S (2011) Reduced reference MPEG2 picture quality measure based on ratio of DCT coefficients. Electron Lett 47(6):382–383

    Article  Google Scholar 

  11. Altous S, Samee MK, Gotze J (2011) Reduced reference image quality assessment for JPEG distortion. In: Proceedings of 2011 ELMAR, pp 97–100

  12. Halim A, Gunawan IP (2011) Haar wavelet based reduced reference quality assessment technique for JPEG/JPEG2000 images. In: 2nd International Conference on Instrumentation Control and Automation (ICA):92–97

  13. Tan KT, Ghanbari M (2000) Blockiness detection for MPEG2-coded video. IEEE Signal Process Lett 7(8):213–215

    Article  Google Scholar 

  14. Kusuma TM, Zepernick HJ (2003) On perceptual objective quality metrics for in-service picture quality monitoring. In: Third ATcrc Telecommunications and networking conference and workshop. Melbourne, Australia

  15. Le Callet P, Viard-Gaudin C, Barba D (2005) Continuous quality assessment of MPEG2 video with reduced reference. In: Proceeding of intenational workshop on video processing and quality metrics for consumer electronics

  16. Yang S (2011) Reduced reference MPEG-2 picture quality measure based on ratio of DCT coefficients. Electron Lett 47(6):382–383

    Article  Google Scholar 

  17. Lin M, Li S, Ngi Ngan K (2013) Reduced-reference image quality assessment in reorganized DCT domain. Signal Process Image Commun 28(8):884–902

    Article  Google Scholar 

  18. Rehman A, Wang Z (2010) Reduced-reference SSIM estimation. In: Proceeding of IEEE International Conference on Image Processing: 282–292

  19. Rehman A, Wang Z (2012) Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans Image Process 21(8):3378–3389

    Article  MathSciNet  Google Scholar 

  20. Xue W, Mou X (2010) Reduced reference image quality assessment based on weibull statistics. In: Proceeding of international workshop on quality of multimedia experience (QoMEX), pp 1–6

  21. Zhang M, Xue W, Mou X (2011) Reduced reference image quality assessment based on statistics of edge. In: Proceedings of SPIE 7876

  22. Wang Z, Simoncelli EP (2005) Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. Hum Vis Electron Imaging X Proc SPIE. 5666:149–159

    Article  Google Scholar 

  23. Li Q, Wang Z (2009) Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE J Sel Top Signal Process 3(2):202–211

    Article  Google Scholar 

  24. Li Q, Wang Z (2008) General-purpose reduced-reference image quality assessment based on perceptually and statistically motivated image representation. ICIP 2008. In: 15th IEEE international conference on image processing, pp 1192–1195

  25. Soundararajan R, Bovik AC (2012) RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans Image Process 21(2):517–526

    Article  MathSciNet  Google Scholar 

  26. Wang X, Jiang G, Yu M (2009) Reduced reference image quality assessment based on contourlet domain and natural image statistics. ICIG 2009. In: 15th International Conference on Image and Graphic, pp 45–50

  27. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Article  Google Scholar 

  28. Sheikh HR, Wang Z, Cormack L, Bovik AC (2012) LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality/

  29. VQEG (2009) Final report from the video quality experts group on the validation of reduced-reference and no-reference objective models for standard definition television, phase I. http://www.vqeg.org/

  30. Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F (2009) TID2008—A database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron 10:30–45

    Google Scholar 

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Acknowledgments

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and constructive suggestions. This paper is jointly supported by the National Natural Science Foundation of China (No. 61379101, No. 51104157) and the Natural Science Foundation of Jiangsu Province (No. BK20130209).

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  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu, China

    Tongfeng Sun, Shifei Ding & Wei Chen

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  1. Tongfeng Sun
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  2. Shifei Ding
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Correspondence to Tongfeng Sun.

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Sun, T., Ding, S. & Chen, W. Reduced-reference image quality assessment through SIFT intensity ratio. Int. J. Mach. Learn. & Cyber. 5, 923–931 (2014). https://doi.org/10.1007/s13042-014-0235-3

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