{"title":"基于学习向量量化的自组织映射图像编码中的动态位分配","authors":"J. S. Neto, S.doN. Neto, Francisco Nascimento","doi":"10.1109/MWSCAS.1995.510224","DOIUrl":null,"url":null,"abstract":"A Self-Organizing Map (SOM) Neural Network for dynamic bit allocation in Adaptive Image Transform Coding is presented. The results shown in this paper are for nets with 30, 96 and 128 neurons in the input layer and 100 (10/spl times/10) neurons in the competition layer. The Learning Vector Quantization LVQ1 algorithm was used to enhance the clustering in the map.","PeriodicalId":165081,"journal":{"name":"38th Midwest Symposium on Circuits and Systems. Proceedings","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamic bit allocation in image coding using a Self-Organizing Map with Learning Vector Quantization\",\"authors\":\"J. S. Neto, S.doN. Neto, Francisco Nascimento\",\"doi\":\"10.1109/MWSCAS.1995.510224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Self-Organizing Map (SOM) Neural Network for dynamic bit allocation in Adaptive Image Transform Coding is presented. The results shown in this paper are for nets with 30, 96 and 128 neurons in the input layer and 100 (10/spl times/10) neurons in the competition layer. The Learning Vector Quantization LVQ1 algorithm was used to enhance the clustering in the map.\",\"PeriodicalId\":165081,\"journal\":{\"name\":\"38th Midwest Symposium on Circuits and Systems. Proceedings\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"38th Midwest Symposium on Circuits and Systems. Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS.1995.510224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"38th Midwest Symposium on Circuits and Systems. Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.1995.510224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic bit allocation in image coding using a Self-Organizing Map with Learning Vector Quantization
A Self-Organizing Map (SOM) Neural Network for dynamic bit allocation in Adaptive Image Transform Coding is presented. The results shown in this paper are for nets with 30, 96 and 128 neurons in the input layer and 100 (10/spl times/10) neurons in the competition layer. The Learning Vector Quantization LVQ1 algorithm was used to enhance the clustering in the map.
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