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Near-Lossless PCA-Based Compression of Seabed Surface with Prediction

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Book cover Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

The paper presents a compression method based on Principal Component Analysis applied to reduce the volume of data in seabed Digital Terrain Model. Such data have to be processed in a manner very different from typical digital images because of practical aspects of analysed problem. Hence, the developed algorithm features a variable compression ratio and a possibility to control a maximal reconstruction error. The main objective is to build an orthogonal base and find a number of PCA coefficients representing analysed surface with an acceptable reconstruction accuracy. We present two variants of processing: an iterative compression approach and an approach predicting a number of coefficients before compression starts. It yields much lower computational demand and is faster. The later algorithm employs several statistical measures of an input surface describing its complexity at the prediction stage. Employed, simple classifier based on Classification and Regression Tree do not introduce high additional time overhead. Performed experiments on real data showed high compression ratios, better than for typical DCT-based methods. The possible application of developed method is modern data management system employed in maritime industry.

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Author information

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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Corresponding author

Correspondence to Paweł Forczmański .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Forczmański, P., Maleika, W. (2015). Near-Lossless PCA-Based Compression of Seabed Surface with Prediction. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

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