next up previous
Next: Introduction

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

David A. Badergif

Joseph JáJágif

Rama Chellappagif

Department of Electrical Engineering, and
Institute for Advanced Computer Studies,
University of Maryland, College Park, MD 20742

October 4, 1993


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 up previous
Next: Introduction

David A. Bader