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Order statistic filter banks.

G R Arce1, M Tian

  • 1Dept. of Electr. Eng., Delaware Univ., Newark, DE.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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Order statistic (OS) filter banks offer improved performance over linear filters for non-Gaussian signals, especially in image and video processing. These nonlinear filter banks provide enhanced signal processing by leveraging both spectral and rank information.

Area of Science:

  • Digital Signal Processing
  • Nonlinear Filter Banks
  • Multirate Signal Processing

Background:

  • Linear filter banks are widely used in multirate signal processing but show limitations with non-Gaussian signals.
  • Non-Gaussian processes often exhibit characteristics that linear filters do not adequately capture, leading to suboptimal performance.
  • Existing filter bank designs are primarily based on linear filtering principles.

Purpose of the Study:

  • Introduce a novel class of nonlinear filter banks: order statistic (OS) filter banks.
  • Explore the potential of OS filter banks to overcome limitations of linear filters in specific signal processing applications.
  • Demonstrate the advantages of OS filter banks, particularly for signals with abrupt changes and details.

Main Methods:

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  • Developed OS filter banks by integrating rank-ordering structure with spectral characteristics of input signals.
  • Utilized traditional linear filter banks as foundational components within the OS filter bank framework.
  • Simulated the performance of OS filter banks, including adaptation using the Least Mean Squares (LMS) algorithm.

    • Main Results:

    • OS filter banks provide both frequency and rank information in localized time intervals.
    • OS filter banks subsume linear filter banks, with linear filters being obtainable through linear transformations of OS filters.
    • Simulations showed significant improvements in the learning characteristics of the LMS algorithm when adapted in the OS subband domain.

    Conclusions:

    • OS filter banks represent a powerful nonlinear alternative to linear filter banks, especially for non-Gaussian signals.
    • The integration of rank-ordering structure enhances signal representation, yielding gains in applications like image and video processing.
    • Adapting algorithms like LMS in the OS subband domain can substantially improve filter performance and learning efficiency.