{"title":"Evolutionary design and optimization of Wavelet Transforms for image compression in embedded systems","authors":"R. Salvador, F. Moreno, T. Riesgo, L. Sekanina","doi":"10.1109/AHS.2010.5546265","DOIUrl":null,"url":null,"abstract":"This paper describes the initial studies of an Evolution Strategy aimed at implementation on embedded systems for the evolution of Wavelet Transforms for image compression. Previous works in the literature have already been proved useful for this application, but they are highly computationally intensive. Therefore, the work described here, deals with the simplifications made to those algorithms to reduce their computing requirements. Several optimizations have been done in the evaluation phase and in the EA operators. The results presented show how the proposed algorithm cut outs still allow for good results to be achieved, while effectively reducing the computing requirements.","PeriodicalId":101655,"journal":{"name":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AHS.2010.5546265","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper describes the initial studies of an Evolution Strategy aimed at implementation on embedded systems for the evolution of Wavelet Transforms for image compression. Previous works in the literature have already been proved useful for this application, but they are highly computationally intensive. Therefore, the work described here, deals with the simplifications made to those algorithms to reduce their computing requirements. Several optimizations have been done in the evaluation phase and in the EA operators. The results presented show how the proposed algorithm cut outs still allow for good results to be achieved, while effectively reducing the computing requirements.