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Image coding using wavelet transform.

M Antonini1, M Barlaud, P Mathieu

  • 1CNRS, Univ. de Nice-Sophia Antipolis, Valbonne.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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This study introduces an image compression method using wavelet transforms and vector quantization, optimizing for human visual perception. It enables faster image recognition with progressive transmission.

Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Traditional image compression methods often overlook human visual system (HVS) limitations.
  • Efficiently representing image data while preserving perceptual quality is a key challenge.

Purpose of the Study:

  • To develop an image compression scheme integrating psychovisual features in both spatial and frequency domains.
  • To enhance image compression efficiency and facilitate rapid visual recognition.

Main Methods:

  • Wavelet transform for image decomposition into biorthogonal subclasses at multiple scales.
  • Vector quantization of wavelet coefficients using a multiresolution codebook based on Shannon's rate distortion theory.
  • Noise shaping bit allocation prioritizing perceptually significant details and progressive transmission.

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Main Results:

  • The proposed method effectively utilizes psychovisual properties for compression.
  • Wavelet transform proves well-suited for progressive image transmission, enabling faster reconstruction.
  • Achieved compression balances visual quality with efficient data representation.

Conclusions:

  • The integration of psychovisual features significantly improves image compression performance.
  • Wavelet-based methods offer a robust framework for progressive image transmission and efficient compression.
  • This approach enhances user experience through quicker image recognition at reduced data rates.