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Wavelet filter evaluation for image compression
J D Villasenor1, B Belzer, J Liao
1Dept. of Electr. Eng., California Univ., Los Angeles, CA.
Summary
Selecting the right wavelet filter bank is crucial for image compression quality and system design. This study introduces new metrics beyond regularity for reliable filter evaluation, identifying optimal filters for image coding.
Area of Science:
- Digital Signal Processing
- Image Compression
- Wavelet Theory
Background:
- Filter bank selection critically impacts wavelet compression performance and system design.
- Regularity is an insufficient metric for predicting compression efficiency.
- Existing evaluation methods lack relevance for image coding applications.
Purpose of the Study:
- To develop reliable filter evaluation metrics for wavelet-based image compression.
- To identify optimal biorthogonal wavelet filter banks for image coding.
- To explore novel filter properties beneficial for system design.
Main Methods:
- Characterizing filter banks as linear shift-variant systems.
- Utilizing impulse response and step response alongside regularity for evaluation.
- Systematic evaluation of minimum-order biorthogonal wavelet filter banks (synthesis/analysis pairs < 36 taps).
Main Results:
- Developed novel, reliable filter evaluation metrics relevant to image coding.
- Evaluated over 4300 candidate filter banks.
- Identified and presented superior filter banks for image compression, including new candidates.
Conclusions:
- Impulse and step response provide superior filter evaluation for image compression compared to regularity alone.
- The identified filters offer enhanced performance and attractive properties for image compression system design.
- This systematic approach facilitates the discovery of optimal wavelet filters for digital imaging.
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