{"title":"自适应位分配图像压缩","authors":"J.K Wu , R.E Burge","doi":"10.1016/0146-664X(82)90024-7","DOIUrl":null,"url":null,"abstract":"<div><p>The key to producing data-compressed images of improved fidelity (at a given compression ratio) using the adaptive transform approach is to improve subimage classification. Three simple measures are introduced to minimize inner-class differences based on image energy, directionality, and fineness of local detail. A fast compression scheme incorporating these measures is illustrated by a range of examples.</p></div>","PeriodicalId":100313,"journal":{"name":"Computer Graphics and Image Processing","volume":"19 4","pages":"Pages 392-400"},"PeriodicalIF":0.0000,"publicationDate":"1982-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0146-664X(82)90024-7","citationCount":"9","resultStr":"{\"title\":\"Adaptive bit allocation for image compression\",\"authors\":\"J.K Wu , R.E Burge\",\"doi\":\"10.1016/0146-664X(82)90024-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The key to producing data-compressed images of improved fidelity (at a given compression ratio) using the adaptive transform approach is to improve subimage classification. Three simple measures are introduced to minimize inner-class differences based on image energy, directionality, and fineness of local detail. A fast compression scheme incorporating these measures is illustrated by a range of examples.</p></div>\",\"PeriodicalId\":100313,\"journal\":{\"name\":\"Computer Graphics and Image Processing\",\"volume\":\"19 4\",\"pages\":\"Pages 392-400\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1982-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0146-664X(82)90024-7\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Graphics and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0146664X82900247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Graphics and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0146664X82900247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The key to producing data-compressed images of improved fidelity (at a given compression ratio) using the adaptive transform approach is to improve subimage classification. Three simple measures are introduced to minimize inner-class differences based on image energy, directionality, and fineness of local detail. A fast compression scheme incorporating these measures is illustrated by a range of examples.
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