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Hybrid vector quantization for multiresolution image coding.

R Rinaldo1, G Calvagno

  • 1Dipt. di Elettronica e Inf., Padova Univ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study introduces an image coding scheme using multiresolution redundancy for efficient data compression. It predicts image blocks from lower resolution versions, optimizing data representation.

Area of Science:

  • Digital image processing
  • Signal processing
  • Data compression

Background:

  • Multiresolution image representations offer inherent redundancy.
  • Efficiently exploiting this redundancy is key for advanced image coding.
  • Existing methods may not fully leverage inter-resolution correlations.

Purpose of the Study:

  • To propose a novel coding scheme for images.
  • To exploit redundancy in multiresolution image representations.
  • To improve the efficiency of image compression.

Main Methods:

  • A coding scheme predicting blocks in one subimage from adjacent lower-resolution subimages of the same orientation.
  • Utilizing a codebook built from coefficient vectors within the subband decomposition for prediction.

Related Experiment Videos

  • Employing geometric vector quantization for Laplacian sources when prediction is insufficient.

    • Main Results:

    • Demonstrated effective prediction of image blocks across different resolutions.
    • Successfully built a codebook from the subband decomposition itself.
    • Achieved satisfactory quality through quantization when prediction failed.

    Conclusions:

    • The proposed coding scheme efficiently utilizes multiresolution image redundancy.
    • The integrated prediction and quantization approach offers a robust compression strategy.
    • This method advances the field of image compression through innovative exploitation of image data structures.