{"title":"无损和致损图像压缩的算术编码","authors":"C. D. Hardin, S. Zabele","doi":"10.1109/MDSP.1989.97129","DOIUrl":null,"url":null,"abstract":"Summary form only given. Arithmetic coding has been applied to provide lossless and loss-inducing compression of optical, infrared, and synthetic aperture radar imagery of natural scenes. Several different contexts have been considered, including both predictive and nonpredictive variations, with both image-dependent and image-independent variations. In lossless coding experiments, arithmetic coding algorithms have been shown to outperform comparable variants of both Huffman and Lempel-Ziv-Welch coding algorithms by approximately 0.5 b/pixel. For image-dependent contexts constructed from high-order autoregressive predictors, arithmetic coding algorithms provide compression ratios as high as four. Contexts constructed from lower-order autoregressive predictors provide compression ratios nearly as great as those of the higher-order predictors with favorable computational trades. Compression performance variations have been shown to reflect the inherent sensor-dependent differences in the stochastic structure of the imagery. Arithmetic coding has also been demonstrated to be a valuable addition to loss-inducing compression techniques.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Arithmetic coding for lossless and loss-inducing image compression\",\"authors\":\"C. D. Hardin, S. Zabele\",\"doi\":\"10.1109/MDSP.1989.97129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. Arithmetic coding has been applied to provide lossless and loss-inducing compression of optical, infrared, and synthetic aperture radar imagery of natural scenes. Several different contexts have been considered, including both predictive and nonpredictive variations, with both image-dependent and image-independent variations. In lossless coding experiments, arithmetic coding algorithms have been shown to outperform comparable variants of both Huffman and Lempel-Ziv-Welch coding algorithms by approximately 0.5 b/pixel. For image-dependent contexts constructed from high-order autoregressive predictors, arithmetic coding algorithms provide compression ratios as high as four. Contexts constructed from lower-order autoregressive predictors provide compression ratios nearly as great as those of the higher-order predictors with favorable computational trades. Compression performance variations have been shown to reflect the inherent sensor-dependent differences in the stochastic structure of the imagery. Arithmetic coding has also been demonstrated to be a valuable addition to loss-inducing compression techniques.<<ETX>>\",\"PeriodicalId\":340681,\"journal\":{\"name\":\"Sixth Multidimensional Signal Processing Workshop,\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth Multidimensional Signal Processing Workshop,\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MDSP.1989.97129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth Multidimensional Signal Processing Workshop,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDSP.1989.97129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Arithmetic coding for lossless and loss-inducing image compression
Summary form only given. Arithmetic coding has been applied to provide lossless and loss-inducing compression of optical, infrared, and synthetic aperture radar imagery of natural scenes. Several different contexts have been considered, including both predictive and nonpredictive variations, with both image-dependent and image-independent variations. In lossless coding experiments, arithmetic coding algorithms have been shown to outperform comparable variants of both Huffman and Lempel-Ziv-Welch coding algorithms by approximately 0.5 b/pixel. For image-dependent contexts constructed from high-order autoregressive predictors, arithmetic coding algorithms provide compression ratios as high as four. Contexts constructed from lower-order autoregressive predictors provide compression ratios nearly as great as those of the higher-order predictors with favorable computational trades. Compression performance variations have been shown to reflect the inherent sensor-dependent differences in the stochastic structure of the imagery. Arithmetic coding has also been demonstrated to be a valuable addition to loss-inducing compression techniques.<>