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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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 Coding
Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24 (2009)
Butts, D.A.: How much information is associated with a particular stimulus? Network: Computation in Neural Systems 14, 177–187 (2003)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunications (1991)
De Weese, M.R., Meister, M.: How to measure the information gained from one symbol. Network: Computation in Neural Systems 10, 325–340 (1999)
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)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vision Research 49(10), 1295–1306 (2009)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews: Neuroscience 2(3), 194–203 (2001)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rigau, J., Feixas, M., Sbert, M. (2011). Image Information in Digital Photography. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-22819-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22818-6
Online ISBN: 978-3-642-22819-3
eBook Packages: Computer ScienceComputer Science (R0)