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An image multiresolution representation for lossless and lossy compression.

A Said1, W A Pearlman

  • 1Fac. of Electr. Eng., Campinas State Univ., Sao Paulo.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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We introduce a novel integer-based image transform for efficient lossless and lossy compression. This method achieves superior compression ratios and rate-distortion performance compared to existing techniques.

Area of Science:

  • Digital image processing
  • Data compression algorithms
  • Signal processing

Background:

  • Traditional image compression methods often involve complex computations or trade-offs between lossless and lossy compression.
  • Existing transforms may require floating-point operations, limiting hardware implementation and efficiency.

Purpose of the Study:

  • To develop a new image multiresolution transform suitable for both lossless and lossy compression.
  • To achieve high compression ratios and excellent rate-distortion performance using computationally efficient methods.

Main Methods:

  • A novel integer-based multiresolution transform utilizing only addition and bit-shift operations.
  • Careful scaling and truncation techniques to minimize bit requirements during transformation.

Related Experiment Videos

  • Entropy coding methods designed to exploit the transform's multiresolution structure for progressive transmission.

    • Main Results:

    • The proposed transform yields lower entropy than predictive coding methods of similar complexity.
    • Achieves lossless compression ratios competitive with the best published results.
    • Demonstrates rate-distortion performance comparable to state-of-the-art lossy compression methods.

    Conclusions:

    • The new integer-based transform offers a powerful and efficient approach for both lossless and lossy image compression.
    • The method supports progressive transmission, enabling exact recovery of the original image.
    • This transform represents a significant advancement in image compression technology.