{"title":"有损小波重构的空间/误差权衡","authors":"John C. Frain, R. Bergeron","doi":"10.1117/12.907544","DOIUrl":null,"url":null,"abstract":"Discrete Wavelet Transforms have proven to be a very effective tool for compressing large data sets. Previous \nresearch has sought to select a subset of wavelet coefficients based on a given space constraint. These approaches \nrequire non-negligible overhead to maintain location information associated with the retained coefficients. Our \napproach identifies entire wavelet coefficient subbands that can be eliminated based on minimizing the total error \nintroduced into the reconstruction. We can get further space reduction (with more error) by encoding some or \nall of the saved coefficients as a byte index into a floating point lookup table. We demonstrate how our approach \ncan yield the same global sum error using less space than traditional MR implementations.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"88 1","pages":"82940J"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Space/error tradeoffs for lossy wavelet reconstruction\",\"authors\":\"John C. Frain, R. Bergeron\",\"doi\":\"10.1117/12.907544\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Discrete Wavelet Transforms have proven to be a very effective tool for compressing large data sets. Previous \\nresearch has sought to select a subset of wavelet coefficients based on a given space constraint. These approaches \\nrequire non-negligible overhead to maintain location information associated with the retained coefficients. Our \\napproach identifies entire wavelet coefficient subbands that can be eliminated based on minimizing the total error \\nintroduced into the reconstruction. We can get further space reduction (with more error) by encoding some or \\nall of the saved coefficients as a byte index into a floating point lookup table. We demonstrate how our approach \\ncan yield the same global sum error using less space than traditional MR implementations.\",\"PeriodicalId\":89305,\"journal\":{\"name\":\"Visualization and data analysis\",\"volume\":\"88 1\",\"pages\":\"82940J\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visualization and data analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.907544\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visualization and data analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.907544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Space/error tradeoffs for lossy wavelet reconstruction
Discrete Wavelet Transforms have proven to be a very effective tool for compressing large data sets. Previous
research has sought to select a subset of wavelet coefficients based on a given space constraint. These approaches
require non-negligible overhead to maintain location information associated with the retained coefficients. Our
approach identifies entire wavelet coefficient subbands that can be eliminated based on minimizing the total error
introduced into the reconstruction. We can get further space reduction (with more error) by encoding some or
all of the saved coefficients as a byte index into a floating point lookup table. We demonstrate how our approach
can yield the same global sum error using less space than traditional MR implementations.