A wavelet filter comparison on multiple datasets for signal compression and denoising

Abstract

In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors’ knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.

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Notes

  1. 1.

    For conciseness sake, the curves associated to the SD aerials and textures are not shown here. However, they can be reproduced from Gnutti (2019). We report that they are consistent with the ones provided in this paper.

  2. 2.

    Again, refer to Gnutti (2019) for the HD high precision image set performance.

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Gnutti, A., Guerrini, F., Adami, N. et al. A wavelet filter comparison on multiple datasets for signal compression and denoising. Multidim Syst Sign Process (2021). https://doi.org/10.1007/s11045-020-00753-w

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Keywords

  • Sub-band coding
  • Discrete wavelet transform
  • Wavelet filter comparison
  • Multiresolution analysis