{"title":"有损压缩的分离架构","authors":"Ying-zong Huang, G. Wornell","doi":"10.1109/ITW.2015.7133164","DOIUrl":null,"url":null,"abstract":"High-performance Model-Code Separation (MCS) architectures for lossless compression are practically viable with graphical message-passing in the decoder. This paper extends separation architectures to lossy compression by constructing model-free but semantics-aware encoders and contributes a new inference-friendly low-density hashing quantizer (LDHQ) to support decoding.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Separation architectures for lossy compression\",\"authors\":\"Ying-zong Huang, G. Wornell\",\"doi\":\"10.1109/ITW.2015.7133164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-performance Model-Code Separation (MCS) architectures for lossless compression are practically viable with graphical message-passing in the decoder. This paper extends separation architectures to lossy compression by constructing model-free but semantics-aware encoders and contributes a new inference-friendly low-density hashing quantizer (LDHQ) to support decoding.\",\"PeriodicalId\":174797,\"journal\":{\"name\":\"2015 IEEE Information Theory Workshop (ITW)\",\"volume\":\"2012 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Information Theory Workshop (ITW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITW.2015.7133164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Information Theory Workshop (ITW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW.2015.7133164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High-performance Model-Code Separation (MCS) architectures for lossless compression are practically viable with graphical message-passing in the decoder. This paper extends separation architectures to lossy compression by constructing model-free but semantics-aware encoders and contributes a new inference-friendly low-density hashing quantizer (LDHQ) to support decoding.
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