A probabilistic approach for rate-distortion modeling of multiscale binary shape
The purpose of this paper it to explore the relationship between the rate-distortion (R-D) characteristics of multi scale binary shape and Markov Random Field (MRF) parameters. In our experiments, we consider two prior models. The first MRF model takes into account pair-wise interaction between pels, and for the binary case, is typically referred to as the auto-logistic model; the second MRF model accounts for higher order spatial interactions and is referred to as the Chien model. Experimental results indicate that the auto-logistic model is not sufficient to characterize the R-D characteristics of multi scale binary shape data. However, higher order models, such as the Chien model, do seem feasible. We propose to use the statistical moments of the Chien model as input to a neural network to accurately predict the rate and distortion of the binary shape when coded at various scales.