A probabilistic approach for rate-distortion modeling of multiscale binary shape

Anthony Vetro, Yao Wang, Huifang Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - 2002
Event2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

Other

Other2002 IEEE International Conference on Acoustic, Speech, and Signal Processing
CountryUnited States
CityOrlando, FL
Period5/13/025/17/02

Fingerprint

distribution moments
logistics
Logistics
interactions
Neural networks
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

Vetro, A., Wang, Y., & Sun, H. (2002). A probabilistic approach for rate-distortion modeling of multiscale binary shape. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 4)

A probabilistic approach for rate-distortion modeling of multiscale binary shape. / Vetro, Anthony; Wang, Yao; Sun, Huifang.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 2002.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Vetro, A, Wang, Y & Sun, H 2002, A probabilistic approach for rate-distortion modeling of multiscale binary shape. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 4, 2002 IEEE International Conference on Acoustic, Speech, and Signal Processing, Orlando, FL, United States, 5/13/02.
Vetro A, Wang Y, Sun H. A probabilistic approach for rate-distortion modeling of multiscale binary shape. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4. 2002
Vetro, Anthony ; Wang, Yao ; Sun, Huifang. / A probabilistic approach for rate-distortion modeling of multiscale binary shape. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 2002.
@inproceedings{9d1784697d3e4c419407c9c43b9646df,
title = "A probabilistic approach for rate-distortion modeling of multiscale binary shape",
abstract = "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.",
author = "Anthony Vetro and Yao Wang and Huifang Sun",
year = "2002",
language = "English (US)",
volume = "4",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

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.

UR - http://www.scopus.com/inward/record.url?scp=19244386412&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=19244386412&partnerID=8YFLogxK

M3 - Conference contribution

VL - 4

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -