Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields

This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. Use of ne-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented here enables machine independent scalable algorithms for a number of problems in image processing and analysis. Permission to publish this abstract separately is granted.

[1]  Ronald I. Greenberg,et al.  Randomized Routing on Fat-Trees (Preliminary Version) , 1985, FOCS.

[2]  R. Kashyap.  Univariate and multivariate random field models for images , 1980 .

[3]  Fernand S. Cohen,et al.  Markov random fields for image modelling and analysis , 1986 .

[4]  J. Besag,et al.  On the estimation and testing of spatial interaction in Gaussian lattice processes , 1975 .

[5]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Rama Chellappa,et al.  Stochastic and deterministic networks for texture segmentation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[8]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[9]  D. Cooper,et al.  SimpleParallel Hierarchical andRelaxation Algorithms forSegmenting Noncausal MarkovianRandom Fields , 1987 .

[10]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[11]  Rama Chellappa,et al.  Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise , 1990, IEEE Trans. Inf. Theory.

[12]  Rama Chellappa,et al.  Texture synthesis using 2-D noncausal autoregressive models , 1985, IEEE Trans. Acoust. Speech Signal Process..

[13]  Anil K. Jain,et al.  Random field models in image analysis , 1989 .

[14]  David H. C. Du,et al.  Performance evaluation of the CM-5 interconnection network , 1993, Digest of Papers. Compcon Spring.

[15]  Selim G. Akl,et al.  Design and analysis of parallel algorithms , 1985 .

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  J. Palmer,et al.  Connection Machine model CM-5 system overview , 1992, [Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation.

[18]  Guy E. Blelloch,et al.  A comparison of sorting algorithms for the connection machine CM-2 , 1991, SPAA '91.

[19]  R. Chellappa.  Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing , 1989 .

[20]  Oh-Jin Kwon,et al.  Segmentation-based image compression , 1993 .

[21]  S. Lennart Johnsson,et al.  Cooley-Tukey FFT on the Connection Machine , 1992, Parallel Comput..

[22]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[23]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[24]  H. Kiinsch.  Thermodynamics and Statistical Analysis of Gaussian Random Fields , 1981 .

[25]  W. Daniel Hillis,et al.  The network architecture of the Connection Machine CM-5 (extended abstract) , 1992, SPAA '92.

[26]  R. Chellappa,et al.  On two-dimensional Markov spectral estimation , 1983 .

[27]  S. Sitharama Iyengar,et al.  Introduction to parallel algorithms , 1998, Wiley series on parallel and distributed computing.

[28]  Rama Chellappa,et al.  Texture synthesis and compression using Gaussian-Markov random field models , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Charles E. Leiserson,et al.  Fat-trees: Universal networks for hardware-efficient supercomputing , 1985, IEEE Transactions on Computers.

[30]  Haluk Derin,et al.  The Use of Gibbs Distributions In Image Processing , 1986 .

[31]  Freerk A. Lootsma,et al.  State-of-the-art in parallel nonlinear optimization , 1988, Parallel Comput..

[32]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..