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Related Experiment Videos

Efficient entropy estimation based on doubly stochastic models for quantized wavelet image data.

Matthew D Gaubatz1, Sheila S Hemami

  • 1Department of Electrical and Computer Engineering, Comell University, Ithaca, NY 14853, USA. gaubatz@ece.cornell.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 5, 2007
PubMed
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Accurate wavelet image coding requires understanding rate-quantization (R-Q) curves. A new doubly stochastic model accurately predicts R-Q curves using polynomials, improving entropy estimation speed and accuracy for image compression.

Area of Science:

  • Digital image processing
  • Information theory
  • Signal processing

Background:

  • Wavelet-based image coding aims to represent data efficiently under rate constraints.
  • Accurate rate-quantization (R-Q) curve estimation is crucial for practical image coders.
  • Traditional methods using scalar probability distributions fail at low bitrates due to uncaptured coefficient dependencies.

Purpose of the Study:

  • To develop a more accurate and efficient entropy estimation scheme for wavelet-based image coding.
  • To address the limitations of existing R-Q curve modeling at low bitrates.
  • To improve the speed-accuracy tradeoff in image compression.

Main Methods:

  • Utilized a doubly stochastic generalized Gaussian model to characterize wavelet coefficients.

Related Experiment Videos

  • Investigated the relationship between quantization step size and compressed rate.
  • Developed an entropy estimation scheme based on polynomial fitting of the R-Q relationship.

    • Main Results:

    • Demonstrated that the R-Q relationship can be accurately modeled by a low-degree polynomial in the log of the step size.
    • The proposed entropy estimation scheme provides instantaneous estimates after a data-gathering phase.
    • Estimates achieved were within 3% of the target rate for state-of-the-art coders.

    Conclusions:

    • The doubly stochastic generalized Gaussian model effectively captures dependencies in wavelet coefficients.
    • The polynomial-based entropy estimation offers a superior speed-accuracy tradeoff for wavelet image coding.
    • This approach enhances the performance of practical image compression systems, especially at low bitrates.