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Space-frequency localized image compression.

P W Wong1, S Noyes

  • 1Hewlett-Packard Co., Palo Alto, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
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This study introduces a novel space-frequency partitioning scheme for wavelet-based image compression. This method improves compression efficiency and image quality compared to traditional techniques.

Area of Science:

  • Signal Processing
  • Image Compression
  • Wavelet Theory

Background:

  • Subband and wavelet-based image compression are frequency-oriented techniques.
  • Existing methods utilize frequency domain analysis for image compression.

Purpose of the Study:

  • To propose a novel space-frequency partitioning scheme for wavelet-based image compression.
  • To exploit the spatial and frequency localization properties of wavelets.
  • To optimize bit allocation for quantizer configurations and wavelet coefficients.

Main Methods:

  • A space-frequency partition scheme is introduced.
  • An iterative bit allocation algorithm is developed to minimize mean square error.
  • Scalar quantization is applied to the wavelet coefficients.

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Main Results:

  • The proposed scheme demonstrates improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) versus rate.
  • Encoded images show better compression efficiency compared to traditional subband and wavelet methods.
  • The bit rate for quantizer configurations is effectively managed.

Conclusions:

  • The space-frequency partition scheme offers superior image compression performance.
  • Wavelet-based compression can be enhanced by jointly considering spatial and frequency domains.
  • The iterative bit allocation algorithm is crucial for optimizing compression rates.