Zerotree based algorithms represent the state of the art inwavelet based image coding. At a high level, these algorithms can bedescribed as first sending a map of the locations of the zerocoefficients (the set of zerotree symbols), and then sending the valueof nonzero coefficients. However, the decision of what map to send istypically made using some simplifying assumption on the structure of themap, motivated by some empirically observed property of the data (e.g.,that zero coefficients are likely to appear in tree structured sets). Inthis article, the map of the locations of the zero coefficients isoptimally estimated as a hidden binary Markov random field (MRF).Algorithms are presented for the estimation of the hidden field giventhe observed wavelet coefficients, for encoding the field, and forencoding the data given the field estimate. Simulation results show avery competitive rate/distortion performance of the coding algorithm,equal or superior to any published zerotree based image coder: this factprovides conclusive empirical evidence that the proposed model isappropriate for the data
展开▼
