Scalable Data Parallel Algorithms for Texture Synthesis and
Compression using Gibbs Random Fields
Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields
Publication or External Link
Bader, David A.
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.)