A new procedure for efficient compression of digital information for storage and transmission purposes involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The vector quantizer design optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The method is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. The authors discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research.<>
{"title":"Wavelet transform-vector quantization compression of supercomputer ocean models","authors":"J. Bradley, C. Brislawn","doi":"10.1109/DCC.1993.253127","DOIUrl":"https://doi.org/10.1109/DCC.1993.253127","url":null,"abstract":"A new procedure for efficient compression of digital information for storage and transmission purposes involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The vector quantizer design optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The method is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. The authors discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research.<<ETX>>","PeriodicalId":315077,"journal":{"name":"[Proceedings] DCC `93: Data Compression Conference","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133837096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The approach is based on some advances in the area of variable rate residual vector quantization considered separately, and in conjunction with subband image decomposition. Comparisons illustrate the improvement in performance attributable to this approach relative to the JPEG coding standard.<>
{"title":"Low bit rate coding of Earth science images","authors":"F. Kossentini, W. Chung, Mark J. T. Smith","doi":"10.1109/DCC.1993.253112","DOIUrl":"https://doi.org/10.1109/DCC.1993.253112","url":null,"abstract":"The approach is based on some advances in the area of variable rate residual vector quantization considered separately, and in conjunction with subband image decomposition. Comparisons illustrate the improvement in performance attributable to this approach relative to the JPEG coding standard.<<ETX>>","PeriodicalId":315077,"journal":{"name":"[Proceedings] DCC `93: Data Compression Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The system is guaranteed theoretically to compress to any feasible rate, with as low a distortion rate as required. It also exhibits user selectable compression and error rates, ability to compress general data types, and adaptation to the data source. The compression system is based on a novel family of connectionist algorithms and generative algorithms used in conjunction with new artificial neural network models that permit the determination of a quasi-optimal architecture for compressing a given data source.<>
{"title":"A high performance adaptive image compression system using a generative neural network: DynAmic Neural Network II (DANN II)","authors":"Andres Rios, M. Kabuka","doi":"10.1109/DCC.1993.253129","DOIUrl":"https://doi.org/10.1109/DCC.1993.253129","url":null,"abstract":"The system is guaranteed theoretically to compress to any feasible rate, with as low a distortion rate as required. It also exhibits user selectable compression and error rates, ability to compress general data types, and adaptation to the data source. The compression system is based on a novel family of connectionist algorithms and generative algorithms used in conjunction with new artificial neural network models that permit the determination of a quasi-optimal architecture for compressing a given data source.<<ETX>>","PeriodicalId":315077,"journal":{"name":"[Proceedings] DCC `93: Data Compression Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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