{"title":"最大误差可控的高光谱图像压缩算法参数优化","authors":"Qiufu Li, Derong Chen, Jiulu Gong","doi":"10.1109/ICIST.2014.6920391","DOIUrl":null,"url":null,"abstract":"In order to improve the efficiency of algorithm, parameter optimization for hyperspectral image compression algorithm of maximum error controllable has been studied in this paper. Firstly, a mathematic optimal model for the hyperspectral image compression ratio was established. Secondly, we analyzed the model and simplified it by Gaussian function. Finally, some real hyperspectral images' compression ratios were estimated using the model. Experiments show the relative error between the estimations and the simulation results is less than 5%, and 31.25% of the both results are exactly the same. In addition, the optimal model saves 70% of running time. These illustrate the high effectiveness and practicability of the optimal model.","PeriodicalId":306383,"journal":{"name":"2014 4th IEEE International Conference on Information Science and Technology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Parameter optimization for hyperspectral image compression algorithm of maximum error controllable\",\"authors\":\"Qiufu Li, Derong Chen, Jiulu Gong\",\"doi\":\"10.1109/ICIST.2014.6920391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the efficiency of algorithm, parameter optimization for hyperspectral image compression algorithm of maximum error controllable has been studied in this paper. Firstly, a mathematic optimal model for the hyperspectral image compression ratio was established. Secondly, we analyzed the model and simplified it by Gaussian function. Finally, some real hyperspectral images' compression ratios were estimated using the model. Experiments show the relative error between the estimations and the simulation results is less than 5%, and 31.25% of the both results are exactly the same. In addition, the optimal model saves 70% of running time. These illustrate the high effectiveness and practicability of the optimal model.\",\"PeriodicalId\":306383,\"journal\":{\"name\":\"2014 4th IEEE International Conference on Information Science and Technology\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 4th IEEE International Conference on Information Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIST.2014.6920391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 4th IEEE International Conference on Information Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2014.6920391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter optimization for hyperspectral image compression algorithm of maximum error controllable
In order to improve the efficiency of algorithm, parameter optimization for hyperspectral image compression algorithm of maximum error controllable has been studied in this paper. Firstly, a mathematic optimal model for the hyperspectral image compression ratio was established. Secondly, we analyzed the model and simplified it by Gaussian function. Finally, some real hyperspectral images' compression ratios were estimated using the model. Experiments show the relative error between the estimations and the simulation results is less than 5%, and 31.25% of the both results are exactly the same. In addition, the optimal model saves 70% of running time. These illustrate the high effectiveness and practicability of the optimal model.
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