skip to main content
article

Methods for the assessment of fused images

Published:01 July 2006Publication History
Skip Abstract Section

Abstract

The prevalence of image fusion---the combination of images of different modalities, such as visible and infrared radiation---has increased the demand for accurate methods of image-quality assessment. The current study used a signal-detection paradigm, identifying the presence or absence of a target in briefly presented images followed by an energy mask, which was compared with computational metric and subjective quality assessment results. In Study 1, 18 participants were presented with fused infrared-visible light images, with a soldier either present or not. Two independent variables, image-fusion method (averaging, contrast pyramid, dual-tree complex wavelet transform) and JPEG compression (no compression, low and high compression), were used in a repeated-measures design. Participants were presented with images and asked to state whether or not they detected the target. In addition, subjective ratings and metric results were obtained. This process was repeated in Study 2, using JPEG2000 compression. The results showed a significant effect for fusion but not compression in JPEG2000 images, while JPEG images showed significant effects for both fusion and compression. Subjective ratings differed, especially for JPEG2000 images, while metric results for both JPEG and JPEG2000 showed similar trends. These results indicate that objective and subjective ratings can differ significantly, and subjective ratings should, therefore, be used with care.

References

  1. Anderson, N. 1974. Algebraic models in perception. In Handbook of Perception: Psychophysical Judgement and Measurement, E. Carterette and M. Friedman, Eds. Vol. 2. Academic Press, New York, 216--298.]]Google ScholarGoogle Scholar
  2. Breitmeyer, B. G. and Ogmen, H. 2000. Recent models and findings in backward visual masking: A comparison, review, and update. Perception & Psychophysics 62, 8, 1572--1595.]]Google ScholarGoogle ScholarCross RefCross Ref
  3. Canga, E. F., Dixon, T. D., Nikolov, S. G., Canagarajah, C. N., Bull, D. R., Noyes, J. M., and Troscianko, T. 2005. Characterisation of image-fusion quality metrics for surveillance applications over bandlimited channels. Proceedings of the Eighth International Conference on Information Fusion 1, 483--490.]]Google ScholarGoogle Scholar
  4. CCIR Recommendation 500-3. 1986. Method for the subjective assessment of the quality of television pictures. Tech. rep., International Telecommunications Union, Geneva, Switzerland.]]Google ScholarGoogle Scholar
  5. Cedrus. 1999. Superlab pro version 2.0. website http://www.superlab.com/.]]Google ScholarGoogle Scholar
  6. de Ridder, H. and Majoor, G. 1990. Numerical category scaling: An efficient method for assessing digital image coding impairments. In Proceedings of SPIE Conference on Human Vision and Electronic Imaging: Models, Methods and Applications. SPIE, Santa Clara, CA. 65--77.]]Google ScholarGoogle Scholar
  7. Dixon, T., Canga, E., Noyes, J., Troscianko, T., Bull, D., and Canagarajah, C. 2005. Psychophysical and metric assessment of fused-images. Proceedings of the Second Symposium on Applied Perception in Graphics and Visualisation. 43--50.]] Google ScholarGoogle Scholar
  8. Eskicioglu, A. 2000. Quality measurement for monochorome compressed images in the past 25 years. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1907--1910.]] Google ScholarGoogle Scholar
  9. Ghafourian, M. and Huang, C. 1995. Comparison between several adaptive search vector quantization schemes and jpeg standard for image compression. IEEE Transactions on Communications 43, 24 (Feb./Mar./Apr.), 1308--1312.]]Google ScholarGoogle ScholarCross RefCross Ref
  10. Green, D. M. and Swets, J. A. 1966. Signal Detection Theory and Psychophysics. Wiley, New York.]]Google ScholarGoogle Scholar
  11. Guihong, Q., Dali, Z., and Pingfan, Y. 2001. Medical image-fusion by wavelet transform modulus maxima. Optics Express 9, 4 (Aug.), 184--190.]]Google ScholarGoogle ScholarCross RefCross Ref
  12. ITU-R Recommendation BT. 500-10. 2000. Methodology for the subjective assessment of the quality of television pictures. Tech. Rep., International Telecommunications Union, Geneva, Switzerland.]]Google ScholarGoogle Scholar
  13. Kingsbury, N. 1999. Image processing with complex wavelets. In Wavelets: The Key to Intermittent Information, B. Silverman and J. Vassilicos, Eds. Oxford University Press, New York, 165--185.]]Google ScholarGoogle Scholar
  14. Kingsbury, N. G. 1998. The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters. In IEEE Digital Signal Processing Workshop. 86, (Aug.).]]Google ScholarGoogle Scholar
  15. Loza, A., Dixon, T. D., Canga, E. F., Nikolov, S. G., Bull, D. R., Canagarajah, C. N., Noyes, J. M., and Troscianko, T. 2005. Methods of fused-image analysis and assessment. In Proceedings of the Advanced Study Institute Conference (NATO-ASI 2005): Multisensor Data and Information Processing for Rapid and Robust Situation and Threat Assessment. NATO, Albena, Bulgaria.]]Google ScholarGoogle Scholar
  16. Luce, R. 1986. Response Times: Their Role in Inferring Elementary Mental Organization. Oxford Psychology Series, Vol. 8. Oxford University Press, New York.]]Google ScholarGoogle Scholar
  17. Macmillan, N. and Creelman, C. 1991. Detection Theory: A User's Guide. Cambridge University Press, New York.]]Google ScholarGoogle Scholar
  18. Miyahara, M., Kotani, K., and Algazi, V. 1998. Objective picture quality scale (pqs) for image coding. IEEE Transactions on Communications 46, 9 (Sept.), 1215--1226.]]Google ScholarGoogle ScholarCross RefCross Ref
  19. Nikolov, S., Hill, P., Bull, D., and Canagarajah, N. 2001. Wavelets for image-fusion. In Wavelets in Signal and Image Analysis, A. Petrosian and F. Meyer, Eds. Computational Imaging and Vision Series. Kluwer Academic Publishers, Dordrecht, Boston, MA, 213--244.]]Google ScholarGoogle Scholar
  20. Petrovic, V. and Xydeas, C. 2000. On the effects of sensor noise in pixel-level image-fusion performance. Proceedings of the Third International Conference on Information Fusion 2, 14--19.]]Google ScholarGoogle Scholar
  21. Petrovic, V. and Xydeas, C. 2003. Sensor noise effects on signal-level image-fusion performance. Information Fusion 4, 3 (Sept.), 167--183.]]Google ScholarGoogle ScholarCross RefCross Ref
  22. Piella, G. 2003. A general framework for multiresolution image-fusion: From pixels to regions. Information Fusion 4, 4 (Dec.), 259--280.]]Google ScholarGoogle ScholarCross RefCross Ref
  23. Piella, G. and Heijmans, H. A. 2003. A new quality metric for image-fusion. International Conference on Image Processing, ICIP, Barcelona.]]Google ScholarGoogle Scholar
  24. Pohl, C. and Van Genderen, J. 1998. Multisensor image-fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing 19, 5, 823--854.]]Google ScholarGoogle ScholarCross RefCross Ref
  25. Rajae-Joordens, R. and Engel, J. 2005. Paired comparisons in visual perception studies using small sample sizes. Displays 26, 1, 1--7.]]Google ScholarGoogle ScholarCross RefCross Ref
  26. Rigolin, V., Robiolio, P., Spero, L.A., Harrawood, B., Morris, K., Fortin, D., Baker, W., Bashore, T., and Cusma, J. 1996. Compression of digital coronary angiograms does not affect visual or quantitative assessment of coronary artery stenosis severity. American Journal of Cardiology 78, 2 (July), 131--135.]]Google ScholarGoogle ScholarCross RefCross Ref
  27. Samet, A., Ayed, M., Loulou, M., and Masmoudi, N. 2004. Perceptual evaluation of jpeg2000. European Transactions on Telecommunications 15, 135--143.]]Google ScholarGoogle ScholarCross RefCross Ref
  28. Santa-Cruz, D., Grosbois, R., and Ebrahimi, T. 2002. Jpeg 2000 performance evaluation and assessment. Signal Processing: Image Communication 17, 1 (Jan.), 113--130.]]Google ScholarGoogle ScholarCross RefCross Ref
  29. Skodras, A. N., Christopoulos, C. A., and Ebrahimi, T. 2001. Jpeg2000: The upcoming still image compression standard. Pattern Recognition Letters 22, 12 (Oct.), 1337--1345.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Steingrímsson, U. and Simon, K. 2003. Perceptive quality estimations: Jpeg 2000 versus jpeg. Journal of Imaging Science and Technology 47, 6 (Nov./Dec.), 572--585.]]Google ScholarGoogle Scholar
  31. Stiles, P. and Hofmann, M. 1997. Demonstrated value of data fusion. American Helicopter Society Avionics and Crew Systems Technical Specialist's Meeting, 23--25.]]Google ScholarGoogle Scholar
  32. Sung, M., Kim, H., Kim, E., Kwak, J., Yoo, J., and Yoo, H. 2002. Clinical evaluation of jpeg2000 compression for digital mammography. IEEE Transactions on Nuclear Science 49, 3, 827--832.]]Google ScholarGoogle ScholarCross RefCross Ref
  33. Taubman, D. and Marcellin, M. 2002. JPEG2000: Image Compression Fundamentals, Standards and Practice. Kluwer International Series in Engineering and Computer Science. Kluwer Academic Publishers, Boston, MA.]] Google ScholarGoogle Scholar
  34. Thurstone, L. 1927. A law of comparative judgement. Psychological Review 34, 273--286.]]Google ScholarGoogle ScholarCross RefCross Ref
  35. Toet, A. 1990. Hierarchical image-fusion. Machine Vision and Applications 3, 1--11.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Toet, A. 1992. Multiscale contrast enhancement with applications to image-fusion. Optical Engineering 31, 1026--1031.]]Google ScholarGoogle ScholarCross RefCross Ref
  37. Toet, A. and Franken, E. M. 2003. Perceptual evaluation of different image-fusion schemes. Displays 24, 1 (Feb.), 25--37.]]Google ScholarGoogle ScholarCross RefCross Ref
  38. Toet, A., IJspeert, J., Waxman, A., and Aguilar, M. 1997. Fusion of visible and thermal imagery improves situational awareness. Displays 18, 85--95.]]Google ScholarGoogle ScholarCross RefCross Ref
  39. Tomura, N., Watanabe, O., Omachi, K., Sakuma, I., Takahashi, S., Otani, T., Kidani, H., and Watarai, J. 2004. Image fusion of thallium-201 spect and mr imaging for the assessment of recurrent head and neck tumors following flap reconstructive surgery. European Radiology 14, 7 (July), 1249--1254.]]Google ScholarGoogle ScholarCross RefCross Ref
  40. Van Dijk, A. and Martens, J. 1997. Subjective quality assessment of compressed images. Signal Processing 58, 235--252.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Wald, L. 2002. Data Fusion: Definitions and Architectures---Fusion of images of different spatial resolutions. Les Presses de l'Ecole des Mines, Paris.]]Google ScholarGoogle Scholar
  42. Wallace, G. 1992. The jpeg still picture compression standard. IEEE Transactions on Consumer Electronics 38, 1 (Feb.), 18--34.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Wang, Z. and Bovik, A. 2002. A universal image quality index. Signal Processing Letters, IEEE 9, 3 (Mar.), 81--84.]]Google ScholarGoogle Scholar
  44. Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (Apr.), 600--612.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Wickens, T. D. 2002. Elementary Signal Detection Theory. Oxford University Press, New York.]]Google ScholarGoogle Scholar
  46. Wilson, T., Rogers, S., and Kabrisky, M. 1995. Perceptual based hyperspectral image fusion using multi---spectral analysis. Optical Engineering 34, 11, 3154--3164.]]Google ScholarGoogle ScholarCross RefCross Ref
  47. Wixted, J. and Lee, K. 2004. Signal detection theory. http://psy.ucsd.edu/~kang/sdt/sdt.htm.]]Google ScholarGoogle Scholar
  48. www.ImageFusion.org. The online resource for research in image-fusion. http://www.imagefusion.org.]]Google ScholarGoogle Scholar

Index Terms

  1. Methods for the assessment of fused images

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Applied Perception
          ACM Transactions on Applied Perception  Volume 3, Issue 3
          July 2006
          180 pages
          ISSN:1544-3558
          EISSN:1544-3965
          DOI:10.1145/1166087
          Issue’s Table of Contents

          Copyright © 2006 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 July 2006
          Published in tap Volume 3, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader