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Distortion estimators for bitplane image coding.

Francesc Aulí-Llinàs1, Michael W Marcellin

  • 1Department of Information and Communications Engineering, Universitat Autonoma de Barcelona, Barcelona, 08290 Cerdanyola del Valles, Spain. fauli@deic.uab.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 9, 2009
PubMed
Summary
This summary is machine-generated.

New estimators improve distortion approximation in bitplane image coding. This advancement enhances lossy compression by accurately modeling coefficient distribution, outperforming current methods.

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Area of Science:

  • Digital image processing
  • Information theory
  • Data compression

Background:

  • Bitplane coding is a standard technique for lossy and lossy-to-lossless image compression.
  • Existing distortion estimators in bitplane coding often rely on inaccurate uniform distribution assumptions for coefficients.
  • Accurate distortion estimation is crucial for optimizing successive data coding and transmission.

Purpose of the Study:

  • To develop novel estimators for approximating distortion in bitplane image coding.
  • To improve the accuracy of distortion estimation by precisely approximating coefficient distributions within quantization intervals.

Main Methods:

  • Introduced new estimators for distortion approximation in bitplane image coders.
  • Utilized precise approximations of transform coefficient distributions within quantization intervals.
  • Conducted experiments across three distinct applications.

Main Results:

  • The proposed estimators demonstrated very high accuracy in approximating distortion.
  • Achieved significant performance improvements compared to existing state-of-the-art methods.
  • Validated the effectiveness of the new estimators in practical scenarios.

Conclusions:

  • The novel estimators provide a more accurate method for distortion approximation in bitplane image coding.
  • This research offers a substantial advancement over current distortion estimation techniques.
  • The findings have direct implications for optimizing image compression algorithms.