{"title":"Image Coding throughDLattice Quantization of Wavelet Coefficients","authors":"Mikhail Shnaider , Andrew P. Papliński","doi":"10.1006/gmip.1997.0429","DOIUrl":null,"url":null,"abstract":"<div><p>The combination of the wavelet transform and vector quantization has proven to be a powerful technique for image compression. In this paper we discuss an image compression system based on the biorthogonal wavelet transform and lattice vector quantizers. In particular, we consider<em>D</em>-type lattices which, as it is shown, are well suited for encoding the wavelet coefficients. In the experimental part of this work the presented image coding system is tested using general-type images as well as fingerprints. The comparison of the fingerprint coding results generated by the presented method with the FBI image compression standard has shown that our method attains a superior speed of coding while maintaining similar figures for signal-to-noise ratio vs compression ratio.</p></div>","PeriodicalId":100591,"journal":{"name":"Graphical Models and Image Processing","volume":"59 4","pages":"Pages 193-204"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/gmip.1997.0429","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077316997904299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The combination of the wavelet transform and vector quantization has proven to be a powerful technique for image compression. In this paper we discuss an image compression system based on the biorthogonal wavelet transform and lattice vector quantizers. In particular, we considerD-type lattices which, as it is shown, are well suited for encoding the wavelet coefficients. In the experimental part of this work the presented image coding system is tested using general-type images as well as fingerprints. The comparison of the fingerprint coding results generated by the presented method with the FBI image compression standard has shown that our method attains a superior speed of coding while maintaining similar figures for signal-to-noise ratio vs compression ratio.
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