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

**
David A. Bader **

` dbader@umiacs.umd.edu`

Joseph JáJá

` joseph@src.umd.edu`

Rama Chellappa

` chella@eng.umd.edu`

Department of Electrical Engineering, and

Institute for Advanced Computer Studies,

University of Maryland, College Park, MD 20742

**October 4, 1993**

### Abstract:

*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 fine-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.

** Keywords:** Gibbs Sampler, Gaussian Markov Random Fields, Image
Processing, Texture Synthesis, Texture Compression, Scalable Parallel
Processing, Data Parallel Algorithms.

** Next:** Introduction

David A. Bader

dbader@umiacs.umd.edu