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Predicting the Number of DCT Coefficients in the Process of Seabed Data Compression

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9256))

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Abstract

The paper presents Discrete Cosine Transform-based compression method applied to data describing seabed topography. It is an improvement over the previously developed and described algorithms capable of variable compression ratio and a possibility to limit the maximal reconstruction error. The main objective is to find an optimal number of DCT coefficients representing a surface with an acceptable reconstruction accuracy. In the original approach the compression was performed in an iterative manner, where successive values were tested, yielding high computational cost and time overhead. The algorithm presented in this paper allows to predict a number of DCT coefficients based on characteristics of specific input surface. Such characteristics are statistical measures describing a complexity of the surface. The classification using simple, fast and easy to learn classifiers does not introduce additional computational overhead. Presented experiments performed on real data gathered by maritime office gave encouraging results. Developed method can be employed in modern data storage and management system handling seabed topographic data.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Żołnierska Str. 52, 71–210, Szczecin, Poland

    Paweł Forczmański & Wojciech Maleika

Authors
  1. Paweł Forczmański
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  2. Wojciech Maleika
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Correspondence to Paweł Forczmański .

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Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Forczmański, P., Maleika, W. (2015). Predicting the Number of DCT Coefficients in the Process of Seabed Data Compression. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-23192-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23191-4

  • Online ISBN: 978-3-319-23192-1

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