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Adaptive partially hidden Markov models with application to bilevel image coding.

S Forchhammer1, T S Rasmussen

  • 1Inst. of Telecommun., Tech. Univ., Lyngby, Lyngby, Denmark. sf@tele.dtu.dk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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Partially hidden Markov models (PHMMs) offer advanced image modeling by conditioning probabilities on past data. These adaptive PHMMs achieve superior lossless bilevel image coding performance compared to existing standards.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Hidden Markov Models (HMMs) are established for sequential data.
  • Standard HMMs do not inherently capture 2D image dependencies.
  • Partially Hidden Markov Models (PHMMs) were previously introduced to address this.

Purpose of the Study:

  • To extend PHMMs for multiple sequences and adaptive coding.
  • To apply these extended PHMMs to lossless bilevel image coding.
  • To optimize PHMMs for reduced model complexity and improved coding efficiency.

Main Methods:

  • Developed multiple token and adaptive versions of PHMMs.
  • Organized contexts in trees to reduce model size.
  • Introduced effective parameter quantization for optimization.

Related Experiment Videos

  • Applied PHMMs to lossless bilevel image compression.

    • Main Results:

    • Achieved compression results superior to the JBIG standard on test images.
    • Demonstrated the effectiveness of adaptive PHMMs for image coding.
    • Showcased improved performance at the cost of increased computational complexity.

    Conclusions:

    • PHMMs provide a novel and effective approach to image modeling.
    • Optimized PHMMs offer a pathway for enhanced lossless image compression.
    • The minimum description length principle can guide PHMM training for tasks like image segmentation and recognition.