A Binary Markov Model for the Quantized Wavelet Coefficients of Images and Its Rate/Distortion Optimization
S.D. Servetto, J.M. Rosenblatt, Kannan Ramchandran · 1997
Zerotree based algorithms represent the state of the art in wavelet based image coding. At a high level, these algorithms can be described as first sending some map of locations of zero coefficients (the set of zerotree symbols), and then sending the value of nonzero coefficients. However, the decision of what map to send is typically made using some simplifying assumption on the structure of the map, motivated by some empirically observed property of the data (e.g., that zero coefficients are likely to appear in tree structured sets): in this work, the map of locations of zero coefficients is optimally estimated as a hidden binary Markov Random Field (MRF) instead. Algorithms are presented for the estimation of the hidden field given the observed wavelet coefficients, for encoding the field, and for encoding the data given the field estimate. Simulation results show very competitive rate/distortion performance of the coding algorithm, equal or superior to any published Zerotree based ...