Numerical Algorithms

, Volume 5, Issue 3, pp 165–177 | Cite as

Iterative polynomial interpolation and data compression

  • Morten DÆhlen
  • Michael Floater


In this paper we look at some iterative interpolation schemes and investigate how they may be used in data compression. In particular, we use the pointwise polynomial interpolation method to decompose discrete data into a sequence of difference vectors. By compressing these differences, one can store an approximation to the data within a specified tolerance using a fraction of the original storage space (the larger the tolerance, the smaller the fraction).

We review the iterative interpolation scheme, describe the decomposition algorithm and present some numerical examples. The numerical results are that the best compression rate (ratio of non-zero data in the approximation to the data in the original) is often attained by using cubic polynomials and in some cases polynomials of higher degree.


Interpolation Method Storage Space Data Compression Interpolation Scheme Polynomial Interpolation 
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

© J.C. Baltzer AG, Science Publishers 1993

Authors and Affiliations

  • Morten DÆhlen
    • 1
  • Michael Floater
    • 1
  1. 1.SINTEF-SIOsloNorway

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