JJAP Conference Proceedings

JJAP Conf. Proc. 4, 011607 (2016) doi:10.7567/JJAPCP.4.011607

Fuzzy information measure for image quality improvement

Annamária R. Várkonyi-Kóczy1,2,3, János T. Tóth3

  1. 1Institute of Mechatronics and Vehicle Engineering, Óbuda University, Budapest, Hungary
  2. 2Integrated Intelligent Systems Japanese-Hungarian Laboratory
  3. 3Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia
  • Received September 26, 2015
  • PDF (941 KB) |

Abstract

Digital image processing can often improve the quality of visual sensing of images and real world’s scenes however the optimization of the used algorithms is not always an easy task. The suitable parameter settings of the methods often depend on the features of the scenes and may also depend on the aim of the (further) processing. In this paper, a fuzzy information measure is introduced which evaluates the level of information of pictures. The idea behind the technique is that the amount of information in an image is strongly related to the number and complexity of the objects in the image. The primary information about the objects is usually related to their boundaries, i.e., the characteristic, corner and edge, pixels carry the most relevant information about the image content. The measure presented in this paper sums up the fuzzy level of details and this amount is used to scale the qualification and transformation of images. The presented technique can advantageously be built into different image processing algorithms favorably used in Image Quality Improvement and High Dynamic Range Imaging. The qualification measure may also be applied for increasing the color differentiation capability of the human eye and thus, for improving visual sensing.

Creative Commons License Content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References

  1. 1 A. Stockman and L. T. Sharpe, Vision Res. 40, 1711 (2000).
  2. 2 E. H. Adelson, The Cognitive Neurosciences (MIT Press, Cambridge, MA, 2000) 2nd ed., Chap. 24, p. 339.
  3. 3 C. Lu, Y. Cao, and D. Mumford, J. Vis. Commun. Image Represent. 13, 65 (2002).
  4. 4 A. Gray, Modern Differential Geometry of Curves and Surfaces with Mathematica (CRC Press, Boca Raton, FL, 1997) 2nd ed., Sect. 16.5 and Chap. 21, p. 373 and 481.
  5. 5 F. Russo, IEEE Trans. Instrum. Meas. 47, 1428 (1998).
  6. 6 F. Russo, New Advances in Intelligent Signal Processing (Springer, Berlin/Heidelberg, 2011) Vol. 372, Chap. 6, p. 115.
  7. 7 T. Beeler, F. Hahn, D. Bradley, B. Bickel, P. Beardsley, C. Gotsman, R. W. Sumner, and M. Gross, J. ACM Trans. Graphics 30, 75 (2011).
  8. 8 A. Sajeena and K. Sunitha Beevi, Int. J. Res. Eng. Technol. 2, 298 (2013).
  9. 9 V. Vonikakis and I. Andreadis, Proc. 2nd Pac. Rim Symp. Adv. in image and video techn., Santiago, 2007, p. 510.
  10. 10 A. Darmont, High Dynamic Range Imaging: Sensors and Architectures (SPIE Press, Bellingham, WA, 2013) 1st ed., p. 138.
  11. 11 F. Banterle, A. Artusi, K. Debattista, and A. Chalmers, Advanced High Dynamic Range Imaging: Theory and Practice (AK Peters (CRC Press), Natick, 2011) p. 278.
  12. 12 I. Langer and A. R. Várkonyi-Kóczy, Proc. IEEE Int. Symp. Med. Meas. and Appl., Budapest, 2012, p. 198.