Abstract
Many desirable properties make fractals a powerful mathematic model applied in several image processing and pattern recognition tasks: image coding, segmentation, feature extraction and indexing, just to cite some of them. Unfortunately, they are based on a strong asymmetric scheme, so suffering from very high coding times. On the other side, linear transforms are quite time balanced, allowing to be usefully integrated in real-time applications, but they do not provide comparable performances with respect to the image quality for high bit rates. Owning to their potential for preserving the original image energy in a few coefficients in the frequency domain, linear transforms also known a widespread diffusion in some side applications such as to select representative features or to define new image quality measures. In this paper, we investigate different levels of embedding linear transforms in a fractal based coding scheme. Experimental results have been organized as to point out what is the contribution of each embedding step to the objective quality of the decoded image.
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References
Avcibas, I., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. Journal of Electronic Imaging 11(2), 206–223 (2002)
Distasi, R., Nappi, M., Tucci, M.: FIRE: Fractal Indexing with Robust Extensions for Image Databases. IEEE Transactions on Image Processing 12(3), 373–384 (2003)
Fisher, Y.: Fractal Image Compression: Theory and Application. Springer, New York (1994)
Komleh, H.E., Chandran, V., Sridharan, S.: Face Recognition Using Fractal. In: ICIP 2001. Proceedings of IEEE International Conference on Image Processing, vol. 3, pp. 58–61. IEEE, Los Alamitos (2001)
Nill, N.B.: A visual model weighted cosine transform for image compression and quality assessment. IEEE Transactions on Communications 3(6), 551–557 (1985)
Distasi, R., Nappi, M., Riccio, D.: A Range/Domain Approximation Error Based Approach for Fractal Image Compression. IEEE Transaction on Image Processing 15(1), 89–97 (2006)
Wohlberg, B., de Jager, G.: Fast image domain fractal compression by DCT domain block matching. Electronics Letters 31(11), 869–870 (1995)
Wu, J.-L., Duh, W.-J.: Feature extraction capability of some discrete transforms. In: Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 5, pp. 2649–2652. IEEE, Los Alamitos (1991)
Kominek, J.: Waterloo BragZone and Fractals Repository (January 25, 2007), http://links.uwaterloo.ca/bragzone.base.html
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© 2007 Springer-Verlag Berlin Heidelberg
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Nappi, M., Riccio, D. (2007). Embedding Linear Transformations in Fractal Image Coding. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_91
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DOI: https://doi.org/10.1007/978-3-540-74607-2_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
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