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Context-based entropy coding of block transform coefficients for image compression.

Chengjie Tu1, Trac D Tran

  • 1Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. cjtu@jhu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces an adaptive block transform image coding algorithm that rivals wavelet coders. The novel approach enhances coding efficiency and performance in image compression.

Area of Science:

  • Digital image processing
  • Data compression algorithms
  • Signal processing

Background:

  • State-of-the-art wavelet image coders significantly outperform traditional block transform coders in rate-distortion (R-D) performance.
  • JPEG2000, a wavelet-based standard, is becoming the benchmark for high-performance still image compression.
  • Existing block transform coders like JPEG have limitations in context modeling and exploiting spatial-frequency correlations.

Purpose of the Study:

  • To investigate the contributions of transform type versus encoding strategy to image coding improvements.
  • To develop a simple, fast, and efficient adaptive block transform image coding algorithm.
  • To achieve competitive R-D performance comparable to advanced wavelet coders.

Main Methods:

Related Experiment Videos

  • Implementation of an adaptive block transform image coding algorithm.
  • Integration of prefiltering and postfiltering techniques.
  • Application of high-order space-frequency context modeling for block transform coefficients.

    • Main Results:

    • The proposed adaptive block transform coder demonstrates competitive R-D performance.
    • The algorithm achieves performance comparable to leading wavelet coders.
    • The coding strategy significantly contributes to the overall coding improvement.

    Conclusions:

    • The developed adaptive block transform coder offers a viable alternative to wavelet-based methods.
    • Simple yet effective context modeling can bridge the performance gap between block and wavelet transforms.
    • The proposed method provides a balance of efficiency, speed, and high-quality image compression.