{"title":"Block-based attractor coding: potential and comparison to vector quantization","authors":"T. Ramstad, S. Lepsøy","doi":"10.1109/ACSSC.1993.342362","DOIUrl":null,"url":null,"abstract":"The paper presents a simple fractal or attractor coder model that has a very fast decoding algorithm and lends itself to comparisons with vector quantization (VQ) of the mean-gain-shape (MGSVQ) type. In fractal theory the transmission of the codebook is somewhat concealed. In our simple model the codebook is explicitly transmitted although with a double role. The main difference between MGSVQ and the fractal coder is that the codebook in MSGV is as statistically optimized from a set of training data whereas it is derived directly from the image to be coded for the fractal coder, and therefore can be viewed as adaptive. Experimental comparisons are given.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1993.342362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
The paper presents a simple fractal or attractor coder model that has a very fast decoding algorithm and lends itself to comparisons with vector quantization (VQ) of the mean-gain-shape (MGSVQ) type. In fractal theory the transmission of the codebook is somewhat concealed. In our simple model the codebook is explicitly transmitted although with a double role. The main difference between MGSVQ and the fractal coder is that the codebook in MSGV is as statistically optimized from a set of training data whereas it is derived directly from the image to be coded for the fractal coder, and therefore can be viewed as adaptive. Experimental comparisons are given.<>
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