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Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis.

M D Adams1, F Kossentni

  • 1Dept. of Electr. and Comput. Eng., British Columbia Univ., Vancouver, BC, Canada. mdadams@ece.ubc.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
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This study compares reversible integer wavelet transforms for image coding, finding that the best choice depends on specific application needs for compression and speed. Reversible transforms offer comparable quality to conventional ones at low bit rates.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Wavelet Theory

Background:

  • Image coding relies on efficient data representation.
  • Wavelet transforms are crucial for image compression.
  • Reversible integer-to-integer transforms offer advantages in precision and efficiency.

Purpose of the Study:

  • To compare reversible integer-to-integer wavelet transforms for image coding.
  • To evaluate their performance in terms of lossy and lossless compression and computational complexity.
  • To compare these reversible transforms against their conventional counterparts.

Main Methods:

  • Comparative analysis of various reversible integer-to-integer wavelet transforms.
  • Evaluation of lossy and lossless compression performance.

Related Experiment Videos

  • Assessment of computational complexity.
  • Comparison with nonreversible real-to-real transforms.

    • Main Results:

    • Several reversible integer-to-integer wavelet transforms demonstrated strong performance.
    • The optimal transform choice is application-dependent based on compression and complexity priorities.
    • Reversible and conventional transforms showed comparable quality at low bit rates.
    • Factors influencing performance were identified through experimental and theoretical analysis.

    Conclusions:

    • Reversible integer-to-integer wavelet transforms are viable and effective for image coding.
    • Performance trade-offs exist, necessitating careful selection based on application requirements.
    • These transforms provide competitive compression efficiency, especially at lower bit rates.