Statistical Modeling & Coding

We are developing efficient methods for statistical modeling and coding with a particular focus on application in video coding. Our research includes the topics of fast binary arithmetic coding, context modeling, probability estimation, and binarization. In addition, we are investigating the concept of probability interval partitioning entropy (PIPE) codes as a low complex, but yet highly efficient variant of binary arithmetic coding.

References

  1. D. Marpe, H. Schwarz, and T. Wiegand, "Context-Based Adaptive Binary Arithmetic Coding in the H.264 / AVC Video Compression Standard," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, No. 7, pp. 620-636, July 2003, invited paper.
    Erratum: On p. 631, left column below Table VI, "max(...)" must be replaced by "min(...)" in the two expressions for specifying context index increments for bins related to absolute values of transform coefficients.
  2. D. Marpe and T. Wiegand, "A Highly Efficient Multiplication-Free Binary Arithmetic Coder and Its Application in Video Coding," Proc. IEEE International Conference on Image Processing (ICIP 2003), Barcelona, Spain, Sept. 2003.
  3. D. Marpe, G. Marten, H. L. Cycon, "A Fast Renormalization Technique for H.264/MPEG4-AVC Arithmetic Coding," Proc. 51st Internationales Wissenschaftliches Kolloquium (IWK), Ilmenau University of Technology, Ilmenau, Germany, September 11-15, 2006.
  4. D. Marpe, H. Schwarz, and T. Wiegand, "Entropy Coding in Video Compression Using Probability Interval Partitioning," Picture Coding Symposium (PCS 2010), Nagoya, Japan, pp. 66-69, December 2010.
  5. H. Kirchhoffer et al., "Probability Interval Partitioning Entropy Coding Using Systematic Variable-to-Variable Length Codes," 18th IEEE International Conference on Image Processing (ICIP 2011), Brussels, Belgium, pp. 333-336, September 2011.