Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
Neural network system for forecasting method selection
Decision Support Systems
Arithmetic coding for data compression
Communications of the ACM
Multispectral Random Field Models for Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A modal symbolic classifier for selecting time series models
Pattern Recognition Letters
A new approach for subset 2-D AR model identification for describing textures
IEEE Transactions on Image Processing
A Gaussian derivative based version of JPEG for image compression and decompression
IEEE Transactions on Image Processing
Recursive estimation of images using non-Gaussian autoregressive models
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
Removing the blocking artifacts of block-based DCT compressed images
IEEE Transactions on Image Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
IEEE Transactions on Image Processing
A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
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In this paper, we propose a family of stochastic models for image compression, where images are assumed to be Gaussian Markov random field. This model is based on stationary full range autoregressive (FRAR) process. The parameters of the model are estimated with the Monte-Carlo integration technique based on Bayesian approach. The advantage of the proposed model is that it helps to estimate the finite number of parameters for the infinite number of orders. We use arithmetic coding to store seed values and parameters of the model as it gives furthermore compression. We also studied the use of Metropolis-Hastings algorithm to update the parameters, through which some image contents such as untexturedness are captured. Different types-both textured and untextured images-are used for experiment to illustrate the efficiency of the proposed model and the results are encouraging.