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Lossless Fractal Image Compression Mechanism by Applying Exact Self-similarities at Same Scale

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Computational Intelligence and Information Technology (CIIT 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 250))

Abstract

Almost all of the fractal image compression methods are lossy because the concept behind these traditional fractal compression methods is based on loose self-similarity, i.e., the transformation function applied on a domain block does not lead to exact matching with range block, instead the principle works on the minimum distance between transformed domain block and range block. Unlike the traditional fractal image compression methods the proposed mechanism does not partition the image into domain blocks and range blocks. Only a single partition of image serves the purpose. The proposed mechanism uses the concept that in most of the cases a small portion of an image is replicated in some other places of the same image; therefore need not to be stored more than once. The paper presents a novel algorithm to achieve lossless fractal image compression with less computation and fast compression and decompression process.

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References

  1. Barnsley, M.: Fractals Everywhere. Academic Press, Inc. (1988)

    Google Scholar 

  2. Jacquin, A.E.: Image Coding Based on a Fractal Theory of Iterated Contractive Image Transformations. IEEE Transactions on Image Processing 1(1) (January 1992)

    Google Scholar 

  3. Zhao, Y., Yuan, B.: A Novel Scheme for Fractal Image Coding. Institute of Information Science Northern Jiaotong University, Beijing, P.R.China (2001)

    Book  Google Scholar 

  4. Wohlberg, B., de Jager, G.: A Review of the Fractal Image Coding Literature (December 1999)

    Google Scholar 

  5. Li, G.: Fast Fractal Image Encoding Based on the Extreme Difference Feature of Normalized Block. College of Computer Science & Technology, Southwest University for Nationalities, Chengdu, China (2009)

    Google Scholar 

  6. Han, J.: Fast Fractal Image Encoding Based on Local Variances and Genetic Algorithm. Dezhou University, China (2009)

    Book  Google Scholar 

  7. Zhao, Y., Hu, J., Chi, D., Li, M.: A Novel Fractal Image Coding based on Basis Block Dictionary. School of Electronics and Information, Shanghai dian ji University, Shanghai, China (2009)

    Google Scholar 

  8. Liu, Y., Sun, J.-g.: Face Recognition Method Based on FLPP. Liaoning Techinical University, Huludao Liaoning, China (2010)

    Google Scholar 

  9. Loganathanff, D., Amudha, J., Mehata, K.M.: Classification and Feature Vector Techniques to Improve Fractal Image Coding. Electrical and Electronics Engineering, Amrita Institute of Technology and Science, Coimbatore, INDIA (2003)

    Google Scholar 

  10. Furao, S., Hasegawa, O.: An Effective Fractal Image Coding Method Without Search, Japan (2004)

    Google Scholar 

  11. Prachumrak, K., Hiramatsu, A., Fuchida, T., Nakamura, H.: Lossless Fractal Image Coding, Croatia (2003)

    Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Kumar, J., Kumar, M. (2011). Lossless Fractal Image Compression Mechanism by Applying Exact Self-similarities at Same Scale. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_101

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  • DOI: https://doi.org/10.1007/978-3-642-25734-6_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25733-9

  • Online ISBN: 978-3-642-25734-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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