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A 2-D orientation-adaptive prediction filter in lifting structures for image coding.

Omer N Gerek1, A Enis Cetin

  • 1Department of Electrical and Electronics Engineering, Anadolu University, Eskişehir TR-26470, Turkey. ongerek@anadolu.edu.tr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 27, 2006
PubMed
Summary

This study introduces an edge-adaptive lifting wavelet for image coding. It efficiently estimates image gradients to improve prediction, especially for diagonal features.

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Area of Science:

  • Digital image processing
  • Wavelet transforms
  • Computer vision

Background:

  • Lifting-style wavelet implementations are crucial for efficient image compression.
  • Existing methods often lack adaptability to local image features like edges.
  • The Daubechies 5/3 wavelet is a common baseline for image coding.

Purpose of the Study:

  • To present a novel two-dimensional (2-D) edge adaptive lifting structure for image coding.
  • To improve prediction accuracy by incorporating image gradient information.
  • To develop a computationally efficient edge estimation technique.

Main Methods:

  • A 2-D lifting structure inspired by the Daubechies 5/3 wavelet was developed.
  • An edge orientation estimator was integrated into the prediction filter.

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  • The prediction domain was designed to rotate +/-45 degrees based on estimated gradients.

    • Main Results:

    • The proposed structure adapts prediction to local image features, including diagonal gradients.
    • The gradient estimator is computationally inexpensive, requiring only six subtractions per lifting step.
    • No multiplications are needed for the gradient estimation, reducing computational load.

    Conclusions:

    • The edge adaptive lifting structure offers improved performance in image coding by adapting to image content.
    • The computationally efficient design makes it suitable for practical image compression applications.
    • This approach enhances wavelet-based image coding through adaptive prediction.