{"title":"面向网络多媒体通信的多描述压缩感知方法","authors":"Liangjun Wang, Xiaolin Wu, Guangming Shi","doi":"10.1109/MMSP.2008.4665120","DOIUrl":null,"url":null,"abstract":"A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A compressive sensing approach of multiple descriptions for network multimedia communication\",\"authors\":\"Liangjun Wang, Xiaolin Wu, Guangming Shi\",\"doi\":\"10.1109/MMSP.2008.4665120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.\",\"PeriodicalId\":402287,\"journal\":{\"name\":\"2008 IEEE 10th Workshop on Multimedia Signal Processing\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE 10th Workshop on Multimedia Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP.2008.4665120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE 10th Workshop on Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2008.4665120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A compressive sensing approach of multiple descriptions for network multimedia communication
A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.