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 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.
(Also cross-referenced as UMIACS-TR-93-80.)