{"title":"自适应图像金字塔表示","authors":"V. Cherkashyn, R. Kountchev, D. He, R. Kountcheva","doi":"10.1109/ISSPIT.2008.4775650","DOIUrl":null,"url":null,"abstract":"New adaptive method for image compression based on pyramid decomposition with neural networks with error back propagation (BPNN) is presented in this paper. The processed image is divided in blocks and then each is compressed in the space of the hidden layers of 3-layer BPNNs, which build the so-called inverse difference pyramid. The results of the new method modeling are compared with these, obtained using the image compression standards JPEG and JPEG2000.","PeriodicalId":213756,"journal":{"name":"2008 IEEE International Symposium on Signal Processing and Information Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Adaptive Image Pyramidal Representation\",\"authors\":\"V. Cherkashyn, R. Kountchev, D. He, R. Kountcheva\",\"doi\":\"10.1109/ISSPIT.2008.4775650\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"New adaptive method for image compression based on pyramid decomposition with neural networks with error back propagation (BPNN) is presented in this paper. The processed image is divided in blocks and then each is compressed in the space of the hidden layers of 3-layer BPNNs, which build the so-called inverse difference pyramid. The results of the new method modeling are compared with these, obtained using the image compression standards JPEG and JPEG2000.\",\"PeriodicalId\":213756,\"journal\":{\"name\":\"2008 IEEE International Symposium on Signal Processing and Information Technology\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Signal Processing and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2008.4775650\",\"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 International Symposium on Signal Processing and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2008.4775650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New adaptive method for image compression based on pyramid decomposition with neural networks with error back propagation (BPNN) is presented in this paper. The processed image is divided in blocks and then each is compressed in the space of the hidden layers of 3-layer BPNNs, which build the so-called inverse difference pyramid. The results of the new method modeling are compared with these, obtained using the image compression standards JPEG and JPEG2000.
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