TY - JOUR
T1 - A probabilistic approach for rate-distortion modeling of multiscale binary shape
AU - Vetro, Anthony
AU - Wang, Yao
AU - Sun, Huifang
PY - 2002
Y1 - 2002
N2 - The purpose of this paper it to explore the relationship between the rate-distortion (R-D) characteristics of multiscale 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 autologistic model is not sufficient to characterize the R-D characteristics of multiscale 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.
AB - The purpose of this paper it to explore the relationship between the rate-distortion (R-D) characteristics of multiscale 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 autologistic model is not sufficient to characterize the R-D characteristics of multiscale 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.
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U2 - 10.1109/ICASSP.2002.5745372
DO - 10.1109/ICASSP.2002.5745372
M3 - Article
AN - SCOPUS:19244386412
SN - 1520-6149
VL - 4
SP - 3353
EP - 3356
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -