Image Information in Digital Photography

  • Jaume Rigau
  • Miquel Feixas
  • Mateu Sbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

Image formation is the process of computing or refining an image from both raw sensor data and prior information. A basic task of image formation is the extraction of the information contained in the sensor data. The information theory provides a mathematical framework to develop measures and algorithms in that process. Based on an information channel between the luminosity and composition of an image, we present three measures to quantify the saliency, specific information, and entanglement of this image associated with its luminance values and regions. The evaluation of these measures could be potentially used as a criterion to achieve more aesthetic or enhanced images.

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References

  1. 1.
    Bezzi, M.: Quantifying the information transmitted in a single stimulus. Biosystems 89(1–3), 4–9 (2007), selected Papers presented at the 6th International Workshop on Neural CodingCrossRefGoogle Scholar
  2. 2.
    Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24 (2009)CrossRefGoogle Scholar
  3. 3.
    Butts, D.A.: How much information is associated with a particular stimulus? Network: Computation in Neural Systems 14, 177–187 (2003)CrossRefGoogle Scholar
  4. 4.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunications (1991)Google Scholar
  5. 5.
    De Weese, M.R., Meister, M.: How to measure the information gained from one symbol. Network: Computation in Neural Systems 10, 325–340 (1999)CrossRefMATHGoogle Scholar
  6. 6.
    Gao, D., Han, S., Vasconcelos, N.: Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 989–1005 (2009)CrossRefGoogle Scholar
  7. 7.
    Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vision Research 49(10), 1295–1306 (2009)CrossRefGoogle Scholar
  8. 8.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews: Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  9. 9.
    O’Sullivan, J.A., Blahut, R.E., Snyder, D.L.: Information-theoretic image formation. In: Information Theory: 50 Years of Discovery, pp. 50–79. IEEE Press, Los Alamitos (2000)Google Scholar
  10. 10.
    Rigau, J., Feixas, M., Sbert, M.: An information theoretic framework for image segmentation. In: IEEE International Conference on Image Processing (ICIP 2004), vol. 2, pp. 1193–1196. IEEE Press, Los Alamitos (2004)Google Scholar
  11. 11.
    Rigau, J., Feixas, M., Sbert, M.: Informational dialogue with Van Gogh’s paintings. In: Brown, P., Cunningham, D.W., Interrante, V., McCormack, J. (eds.) Computational Aesthetics 2008. Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 115–122. Eurographics Association (June 2008)Google Scholar
  12. 12.
    Wallraven, C., Cunningham, D., Rigau, J., Feixas, M., Sbert, M.: Aesthetic appraisal of art — from eye movements to computers. In: Deussen, O., Hall, P., Gibson, S., Hushlack, G., Shaw, J. (eds.) Computational Aesthetics 2009. Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 137–144. Eurographics Association (May 2009)Google Scholar
  13. 13.
    Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: A bayesian framework for saliency using natural statistics. Journal of Vision 8(7), 1–20 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jaume Rigau
    • 1
  • Miquel Feixas
    • 1
  • Mateu Sbert
    • 1
  1. 1.Graphics and Imaging LaboratoryUniversity of GironaSpain

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