{"title":"分形图像压缩的并行实现","authors":"H. Lin, A. Venetsanopoulos","doi":"10.1109/CCECE.1995.526608","DOIUrl":null,"url":null,"abstract":"Fractal image encoding is based on the self-similarity search between range and domain blocks. It can be parallelized in three different ways. In this paper, we first formulate the encoding process and then present one of the parallel implementations on KSRI (Kendall Square Research parallel computer). Experimental results include the encoding time as a function of the number of processors and the speedup curve. The speedup of the encoding process is significant when compared to serial computation.","PeriodicalId":158581,"journal":{"name":"Proceedings 1995 Canadian Conference on Electrical and Computer Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Parallel implementation of fractal image compression\",\"authors\":\"H. Lin, A. Venetsanopoulos\",\"doi\":\"10.1109/CCECE.1995.526608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fractal image encoding is based on the self-similarity search between range and domain blocks. It can be parallelized in three different ways. In this paper, we first formulate the encoding process and then present one of the parallel implementations on KSRI (Kendall Square Research parallel computer). Experimental results include the encoding time as a function of the number of processors and the speedup curve. The speedup of the encoding process is significant when compared to serial computation.\",\"PeriodicalId\":158581,\"journal\":{\"name\":\"Proceedings 1995 Canadian Conference on Electrical and Computer Engineering\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1995 Canadian Conference on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCECE.1995.526608\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1995 Canadian Conference on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCECE.1995.526608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel implementation of fractal image compression
Fractal image encoding is based on the self-similarity search between range and domain blocks. It can be parallelized in three different ways. In this paper, we first formulate the encoding process and then present one of the parallel implementations on KSRI (Kendall Square Research parallel computer). Experimental results include the encoding time as a function of the number of processors and the speedup curve. The speedup of the encoding process is significant when compared to serial computation.
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