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Advances in Image and Video Compression Using Wavelet Transforms and Fovea Centralis

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Abstract

It is well-known that there has been a considerable progress in multimedia technologies during the last decades, namely TV, photography, sound and video recording, communication systems, etc., which came into the world during at least half of the previous century and were developed as analog systems, and nowadays have been almost completely replaced by digital systems. The aforementioned motivates a deep study of multimedia compression and intensive research in this area. Data compression is concerned with minimization of the number of information carrying units used to represent a given data set. Such smaller representation can be achieved by applying coding algorithms. Coding algorithms can be either lossless algorithms that reconstruct the original data set perfectly or lossy algorithms that reconstruct a close representation of the original data set. Both methods can be used together to achieve higher compression ratios. Lossless compression methods can either exploit statistical structure of the data or compress the data by building a dictionary that uses fewer symbols for each string that appears on the data set. Lossy compression, on the other hand, uses a mathematical transform that projects the current data set onto the frequency domain. The coefficients obtained from the transform are quantized and stored. The quantized coefficients require less space to be stored. This chapter is focused on the recently published advances in image and video compression to date considering the use of the integer discrete cosine transform (IDCT), wavelet transforms, and fovea centralis.

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Notes

  1. 1.

    http://www.adobe.com.

  2. 2.

    http://www.gimp.org.

  3. 3.

    https://developers.google.com/speed/webp/?csw=1.

  4. 4.

    JPEG2000 draft at http://www.jpeg.org/public/fcd15444-1.pdf.

  5. 5.

    http://jpeg.org/jbig/index.html.

  6. 6.

    https://media.xiph.org/video/derf/.

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Acknowledgement

The authors gratefully acknowledge the financial support from CONACYT, Mexico.

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Authors and Affiliations

  1. Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla, Cholula, Puebla, Mexico

    Juan C. Galan-Hernandez, Vicente Alarcon-Aquino & Oleg Starostenko

  2. Department of Electronics and Computer Science, Instituto Nacional de Astrofisica, Tonantzintla, Puebla, Mexico

    Juan Manuel Ramirez-Cortes & Pilar Gomez-Gil

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  1. Juan C. Galan-Hernandez
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  2. Vicente Alarcon-Aquino
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Correspondence to Vicente Alarcon-Aquino .

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  1. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Oleg Sergiyenko

  2. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes

  3. Leuphana University of Lueneburg, Lueneburg, Low Saxony, Niedersachsen, Germany

    Paolo Mercorelli

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Galan-Hernandez, J.C., Alarcon-Aquino, V., Starostenko, O., Ramirez-Cortes, J., Gomez-Gil, P. (2020). Advances in Image and Video Compression Using Wavelet Transforms and Fovea Centralis. In: Sergiyenko, O., Flores-Fuentes, W., Mercorelli, P. (eds) Machine Vision and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-030-22587-2_19

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