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Fuzzy Transform for Image and Video Compression

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Fuzzy Transforms for Image Processing and Data Analysis

Abstract

In this chapter, methods based on F-transform are explored and applied to image and video compression. They are considered lossy compression methods, in which the image is rebuilt with a loss of information. An example of a famous lossy image compression algorithm is the Joint Photographic Experts Group (JPEG) method.

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References

  1. Di Martino, F., Loia, V., & Sessa, S. (2003a). A method for coding/decoding images by using fuzzy relation equations. In T. Bilgic, B. De Baets & O. Kaynak (Eds.), Fuzzy sets and systems—IFSA 2003. Lecture Notes in Artificial Intelligence (Vol. 2715, pp. 436–441). Berlin: Springer.

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  2. Di Martino, F., Loia V., & Sessa S. (2003b). A method in the compression/decompression of images using fuzzy equations and fuzzy similarities. In Proceedings of the 10th IFSA World Congress (pp. 524–527). Istanbul, Turkey.

    Google Scholar 

  3. Di Martino, F., Loia, V., Perfilieva, I., & Sessa, S. (2008). An image coding/decoding method based on direct and inverse fuzzy transforms. Fuzzy Sets and Systems, 48(1), 110–131.

    MATH  Google Scholar 

  4. Di Martino, F., Loia, V., & Sessa S. (2010a). Fuzzy transforms for compression and decompression of color videos. Information Sciences, 180, 3914-3931.

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  5. Di Martino, F., Loia V., & Sessa S. (2010b). A segmentation method for images compressed by fuzzy transforms. Fuzzy Sets and Systems, 161(1), 56–74

    Google Scholar 

  6. Di Martino, F., Loia, V., & Sessa S. (2010c). Fuzzy transforms method and attribute dependency in data analysis. Information Sciences, 180(4), 493–505

    Google Scholar 

  7. Di Martino, F., Perfilieva I, & Sessa S. (2017). First order fuzzy transform for images compression. Journal of Signal and Information Processing, 8, 178–194.

    Google Scholar 

  8. Di Martino, F., & Sessa S. (2018). Multi-level fuzzy transforms image compression. Journal of Ambient Intelligence and Humanized Computing, 1–12. https://doi.org/10.1007/s12652-018-0971-4.

  9. Loia, V., & Sessa, S. (2005). Fuzzy relation equations for coding/decoding processes of images and videos. Information Sciences, 171, 145–172.

    Article  MathSciNet  Google Scholar 

  10. Nobuhara, H., Hirota, K. Pedrycz W., & Sessa S. (2006). A motion compression/reconstruction method based on max T-norm composite fuzzy relational equations. Information Sciences, 176(17), 2526–2552.

    Google Scholar 

  11. Nobuhara, H., Pedrycz, W., & Hirota, K. (2005). Relational image compression: optimizations through the design of fuzzy coders and YUV color space. Soft Computing, 9(6), 471–479.

    Article  Google Scholar 

  12. Nobuhara, H., Hirota, K., Di Martino, F., Pedrycz, W., & Sessa, S. (2005). Fuzzy relation equations for compression/decompression processes of colour images in the RGB and YUV colour spaces. Fuzzy Optimization and Decision Making, 4(3), 235–246.

    Article  MathSciNet  Google Scholar 

  13. Pennebaker, W. B., & Mitchell, J. L. (1991). JPEG: Still Image Data Compression Standard (638 pp). Springer. ISBN: 978-0442012724.

    Google Scholar 

  14. Perfilieva, I. (2006). Fuzzy transforms: Theory and applications. Fuzzy Sets and Systems, 157(8), 993–1023.

    Article  MathSciNet  Google Scholar 

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Correspondence to Ferdinando Di Martino .

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Di Martino, F., Sessa, S. (2020). Fuzzy Transform for Image and Video Compression. In: Fuzzy Transforms for Image Processing and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-44613-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-44613-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44612-3

  • Online ISBN: 978-3-030-44613-0

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