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Conditional entropy coding of VQ indexes for image compression.

X Wu1, J Wen, W H Wong

  • 1Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada. xiaolin@ntec-media.de

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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We developed a new method for image compression that significantly reduces data size. This conditional entropy coding of VQ indexes (CECOVI) improves efficiency over existing techniques.

Area of Science:

  • Digital image processing
  • Information theory
  • Machine learning

Background:

  • Practical vector quantization (VQ) image coders have limited block sizes, hindering exploitation of high-order pixel dependencies.
  • Adaptive entropy coding using statistical context modeling can reduce bit rates in VQ coders.

Purpose of the Study:

  • To develop a novel framework for conditional entropy coding of VQ indexes (CECOVI).
  • To improve the efficiency and reduce the computational cost of VQ-based image compression.

Main Methods:

  • Developed a framework for conditional entropy coding of VQ indexes (CECOVI).
  • Utilized a Bayesian-type method for estimating probabilities conditioned on causal contexts.
  • Positioned address-VQ as a special case within the CECOVI framework.

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Main Results:

  • CECOVI is conceptually cleaner and algorithmically more efficient than address-VQ.
  • Achieved over 20% bit rate reduction compared to address-VQ for equivalent distortion levels.
  • Demonstrated significantly lower computational cost compared to address-VQ.

Conclusions:

  • CECOVI offers a more efficient approach to adaptive entropy coding for VQ image compression.
  • The proposed framework provides substantial improvements in bit rate reduction and computational efficiency.
  • CECOVI represents a significant advancement over previous methods like address-VQ.