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CNN-GRNN for Image Sharpness Assessment

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

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Abstract

Image sharpness is key to readability and scene understanding. Because of the inaccessible reference information, blind image sharpness assessment (BISA) is useful and challenging. In this paper, a shallow convolutional neural network (CNN) is proposed for intrinsic representation of image sharpness and general regression neural network (GRNN) is utilized for precise score prediction. The hybrid CNN-GRNN model tends to build functional relationship between retrieved features and subjective human scores by supervised learning. Superior to traditional algorithms based on handcrafted features and machine learning, CNN-GRNN fuses feature extraction and score prediction into an optimization procedure. Experiments on Gaussian blurring images in LIVE, CSIQ, TID2008 and TID2013 show that CNN-GRNN outperforms the state-of-the-art algorithms and gets closer to human subjective judgment.

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References

  1. Bahrami, K., Kot, A.C.: A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Sig. Process. Lett. 21(6), 751–755 (2014)

    Article  Google Scholar 

  2. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  3. Chandler, D.M.: Seven challenges in image quality assessment: past, present, and future research. ISRN Sig. Process. 2013, 1–53 (2013)

    Article  Google Scholar 

  4. Ciancio, A., Costa, A.D., da Silva, E., Said, A., Samadani, R., Obrador, P.: No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Trans. Image Process. 21(3), 934–945 (2012)

    Article  MathSciNet  Google Scholar 

  5. Ferzli, R., Karam, L.J.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18(4), 717–728 (2009)

    Article  MathSciNet  Google Scholar 

  6. Hassen, R., Wang, Z., Salama, M.M.: Image sharpness assessment based on local phase coherence. IEEE Trans. Image Process. 22(7), 2798–2810 (2013)

    Article  Google Scholar 

  7. Hou, W., Gao, X.: Saliency-guided deep framework for image quality assessment. IEEE Multimedia 22(2), 46–55 (2015)

    Article  Google Scholar 

  8. Hou, W., Gao, X., Tao, D., Li, X.: Blind image quality assessment via deep learning. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 46–55 (2015)

    MathSciNet  Google Scholar 

  9. Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740 (2014)

    Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  11. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 11006 (2010)

    Article  Google Scholar 

  12. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  13. Li, C., Bovik, A.C., Wu, X.: Blind image quality assessment using a general regression neural network. IEEE Trans. Neural Netw. 22(5), 793–799 (2011)

    Article  Google Scholar 

  14. Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.C.: No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. 46(1), 39–50 (2016)

    Article  Google Scholar 

  15. Li, Y., Po, L., Xu, X., Feng, L., Yuan, F., Cheung, C.H., Cheung, K.W.: No-reference image quality assessment with shearlet transform and deep neural networks. Neurocomputing 154, 94–109 (2015)

    Article  Google Scholar 

  16. Li, L., Wu, D., Wu, J., Li, H., Lin, W., Kot, A.C.: Image sharpness assessment by sparse representation. IEEE Trans. Multimedia 18(6), 1085–1097 (2016)

    Article  Google Scholar 

  17. Lin, W., Kuo, C.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)

    Article  Google Scholar 

  18. Lv, Q., Jiang, G., Yu, M., Xu, H., Shao, F., Liu, S.: Difference of Gaussian statistical features based blind image quality assessment: a deep learning approach. In: IEEE Conference on Image Processing, pp. 2344–2348 (2015)

    Google Scholar 

  19. Manap, R.A., Shao, L.: Non-distortion-specific no-reference image quality assessment: a survey. Inf. Sci. 301, 141–160 (2015)

    Article  Google Scholar 

  20. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  21. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely” blind image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  22. Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Sig. Process. Lett. 17(5), 513–516 (2010)

    Article  Google Scholar 

  23. Narvekar, N.D., Karam, L.J.: A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Process. 20(9), 2678–2683 (2009)

    Article  MathSciNet  Google Scholar 

  24. Niu, X., Suen, C.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012)

    Article  Google Scholar 

  25. Palm, R.B.: Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmar (2012)

    Google Scholar 

  26. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Astola, J., Carli, M., Battisti, F.: TID2008 - a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10(4), 30–45 (2009)

    Google Scholar 

  27. Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.C.J.: Image database TID2013: peculiarities, results and perspectives. Sig. Process. Image Commun. 20, 57–77 (2015)

    Article  Google Scholar 

  28. Russakovsky, O., Deng, J., Su, H., Jonathan, K., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  29. Saad, M.A., Bovik, A.C., Christophe, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  30. Sang, Q., Qi, H., Wu, X., Bovic, A.C.: No-reference image blur index based on singular value curve. J. Vis. Commun. Image Represent. 25(7), 1625–1630 (2014)

    Article  Google Scholar 

  31. Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)

    Article  Google Scholar 

  32. Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)

    Article  Google Scholar 

  33. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  34. Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P.: CID2013: a database for evaluating no-reference image quality assessment algorithms. IEEE Trans. Image Process. 24(1), 390–402 (2015)

    Article  MathSciNet  Google Scholar 

  35. Vu, P.V., Chandler, D.M.: A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Sig. Process. Lett. 19(7), 423–426 (2012)

    Article  Google Scholar 

  36. Vu, C.T., Phan, T.D., Chandler, D.M.: S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21(3), 934–945 (2012)

    Article  MathSciNet  Google Scholar 

  37. Wang, Z., Bovik, A.C.: Reduced- and no-reference image quality assessment. IEEE Sig. Process. Mag. 28(6), 29–40 (2011)

    Article  Google Scholar 

  38. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  39. Yu, S., Zhang, W., Wu, S., Li, X., Xie, Y.: Applications of edge preservation ratio in image processing. In: IEEE International Conference on Signal Processing, pp. 698–702 (2014)

    Google Scholar 

  40. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

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Acknowledgement

The authors would like to thank reviewers for their valuable suggestion that has helped to improve the paper quality. This work is supported from National Natural Science Foundation of China (Grant No. 81501463 and 61379143), Guangdong Innovative Research Team Program (Grant No. 2011S013), National 863 Programs of China (Grant No. 2015AA043203), the Shenzhen Fundamental Research Program (Grant Nos. JCYJ20140417113430726, JCYJ20140417113430665 and JCYJ20150401145529039), the Qing Lan Project of Jiangsu Province and the China Postdoctoral Science Foundation (Grant No. 2016M590827).

Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Shaode Yu, Fan Jiang & Yaoqin Xie

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China

    Shaode Yu

  3. School of Information and Electronic Engineering, Chinese University of Mining and Technology, Xuzhou, China

    Leida Li

Authors
  1. Shaode Yu
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  2. Fan Jiang
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  3. Leida Li
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  4. Yaoqin Xie
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Corresponding authors

Correspondence to Leida Li or Yaoqin Xie .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Yu, S., Jiang, F., Li, L., Xie, Y. (2017). CNN-GRNN for Image Sharpness Assessment. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-54407-6_4

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