No-Reference Quality Assessment of JPEG Images by Using CBP Neural Networks

  • Paolo Gastaldo
  • Giovanni Parodi
  • Judith Redi
  • Rodolfo Zunino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)

Abstract

Imaging algorithms often require reliable methods to evaluate the quality effects of the visual artifacts that digital processing brings about. This paper adopts a no-reference objective method for predicting the perceived quality of images in a deterministic fashion. Principal Component Analysis is first used to assemble a set of objective features that best characterize the information in image data. Then a neural network, based on the Circular Back-Propagation (CBP) model, associates the selected features with the corresponding predictions of quality ratings and reproduces the scores process of human assessors. The neural model allows one to decouple the process of feature selection from the task of mapping features into a quality score. Results on a public database for an image-quality experiment involving JPEG compressed-images and comparisons with existing objective methods confirm the approach effectiveness.

Keywords

Image quality assessment feedforward neural networks JPEG 

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References

  1. 1.
    International Telecommunication Union: Methodology for the subjective assessment of the quality of television pictures. ITU-R BT.500 (1995)Google Scholar
  2. 2.
    Wang, Z., Sheikh, H.R., Bovik, A.C.: Objective video quality assessment. In: Furth, B., Marques, O. (eds.) The Handbook of Video Databases: Design and Applications, CRC Press, Boca Raton, FL (2003)Google Scholar
  3. 3.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithm. IEEE Trans. Image Processing 15, 3441–3452 (2006)Google Scholar
  4. 4.
    Wang, Z., Sheikh, H.R., Bovik, A.C.: No reference PSNR estimation for compressed pictures. In: Proc. IEEE ICIP, Rochester, September 22-25, pp. 477–480. IEEE Computer Society Press, New York (2002)Google Scholar
  5. 5.
    Pan, F., Lin, X., Rahardja, S., Lin, W., Ong, E., Yao, S., Lu, Z., Yang, X.: A locally adaptive algorithm for measuring blocking artifacts in images and videos. Signal Processing: Image Communication 19, 499–506 (2004)CrossRefGoogle Scholar
  6. 6.
    Gastaldo, P., Zunino, Z.: Neural networks for the no-reference assessment of perceived quality. Journal of Electronic Imaging 14 (2005)Google Scholar
  7. 7.
    Ridella, S., Rovetta, S., Zunino, R.: Circular back-propagation networks for classification. IEEE Trans. on Neural Networks 8, 84–97 (1997)CrossRefGoogle Scholar
  8. 8.
    Huang, J., Ravi Kumar, S., Mitra, M., Zhu, W.-J., Zabih, R.: Image indexing using color correlograms. In: Proc. IEEE CVPR ’97, pp. 762–768 (1997)Google Scholar
  9. 9.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. On Systems, Man and Cybernetics SMC 3, 610–621 (1973)Google Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
  11. 11.
    Rumelhart, D.E., McClelland, J.L.: Parallel distributed processing. MIT Press, Cambridge, MA (1986)Google Scholar
  12. 12.
    Perrone, M.: Improving regression estimates: Averaging methods for variance reduction with extension to general convex measure optimization. Ph.D. dissertation, Phys. Dep., Brown Univ., Providence, RI (1993)Google Scholar
  13. 13.
    Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation. 4, 1–48 (1992)CrossRefGoogle Scholar
  14. 14.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence. 20, 226–239 (1998)CrossRefGoogle Scholar
  15. 15.
    Rovetta, S., Zunino, R.: A multiprocessor-oriented visual tracking system. IEEE Trans. on Industrial Electronics 46, 842–850 (1999)CrossRefGoogle Scholar
  16. 16.
    Sheikh, H.R., Wang, Z., Cormack, L., and Bovik, A.C.: LIVE Image Quality Assessment Database at, http://live.ece.utexas.edu/research/quality
  17. 17.
    Widrow, B., Lehr, M.A.: 30 Years of Adaptive Neural Networks: Perceptron, Madaline and Back Propagation. In: Proc. IEEE, vol. 78, pp. 1415–1442 (1990)Google Scholar
  18. 18.
    Vogl, T.P., Mangis, J.K., Rigler, A.K., Zink, W.T., Alkon, D.L.: Accelerating the convergence of the back propagation method. Biol. Cybern. 59, 257–263 (1998)CrossRefGoogle Scholar
  19. 19.
    Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, Reading, MA (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Paolo Gastaldo
    • 1
  • Giovanni Parodi
    • 1
  • Judith Redi
    • 1
  • Rodolfo Zunino
    • 1
  1. 1.Dept. of Biophysical and Electronic Engineering (DIBE), University of Genoa, Via Opera Pia 11a, 16145, GenoaItaly

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