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

  • Paweł ForczmańskiEmail author
  • Wojciech Maleika
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)


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.


Compression Ratio Reconstruction Error Binary Search High Compression Ratio Reconstruction Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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