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Related Experiment Videos

A wavelet-based two-stage near-lossless coder.

Sehoon Yea1, William A Pearlman

  • 1Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA. yea@merl.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 2, 2006
PubMed
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This study introduces a novel two-stage near-lossless compression method using wavelet transforms and arithmetic coding. It efficiently finds the optimal bit rate for reduced data size while ensuring a defined pixel error bound.

Area of Science:

  • Image Compression
  • Signal Processing
  • Information Theory

Background:

  • Near-lossless compression is crucial for preserving image fidelity while reducing data size.
  • Existing methods often involve iterative processes to optimize compression parameters.
  • The "lossy plus residual coding" approach offers a balance between compression efficiency and accuracy.

Purpose of the Study:

  • To develop an efficient two-stage near-lossless compression scheme.
  • To determine the optimal bit rate allocation between the lossy and lossless stages for minimum total bit rate.
  • To avoid computationally intensive iterative procedures for bit rate optimization.

Main Methods:

  • A two-stage compression scheme combining a wavelet-based lossy layer and arithmetic coding of the residual.

Related Experiment Videos

  • Quantization of the residual to meet a specified L-infinity error bound in the pixel domain.
  • A novel, non-iterative method for estimating the optimal bit rate for the lossy stage.

    • Main Results:

    • The proposed scheme achieves near-lossless compression with a guaranteed error bound.
    • A simple, theoretically justified method for optimal bit rate estimation is presented.
    • The method avoids iterative decoding and inverse discrete wavelet transform, reducing computational complexity.

    Conclusions:

    • The presented two-stage compression scheme offers an efficient and practical solution for near-lossless image compression.
    • The proposed optimal bit rate estimation method simplifies the process and reduces computational overhead.
    • This approach advances the field of image compression by providing a balance of efficiency, accuracy, and speed.