{"title":"基于图像特征分析的小波滤波图像质量压缩分类","authors":"R. Tjahyadi, Wanquan Liu","doi":"10.1109/ICOSP.2002.1181207","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a method to extract features from images that may be related to their compression capability with wavelet filters - their fidelity. Based on these features, images are classified into three different classes corresponding to their fidelity: low, medium and high. We have found this classification schema is effective and can be used as a guideline for selecting wavelet filter for the images in the low fidelity class.","PeriodicalId":159807,"journal":{"name":"6th International Conference on Signal Processing, 2002.","volume":"331 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image classification for quality compression with wavelet filters based on image feature analysis\",\"authors\":\"R. Tjahyadi, Wanquan Liu\",\"doi\":\"10.1109/ICOSP.2002.1181207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a method to extract features from images that may be related to their compression capability with wavelet filters - their fidelity. Based on these features, images are classified into three different classes corresponding to their fidelity: low, medium and high. We have found this classification schema is effective and can be used as a guideline for selecting wavelet filter for the images in the low fidelity class.\",\"PeriodicalId\":159807,\"journal\":{\"name\":\"6th International Conference on Signal Processing, 2002.\",\"volume\":\"331 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"6th International Conference on Signal Processing, 2002.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2002.1181207\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th International Conference on Signal Processing, 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2002.1181207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image classification for quality compression with wavelet filters based on image feature analysis
In this paper, we propose a method to extract features from images that may be related to their compression capability with wavelet filters - their fidelity. Based on these features, images are classified into three different classes corresponding to their fidelity: low, medium and high. We have found this classification schema is effective and can be used as a guideline for selecting wavelet filter for the images in the low fidelity class.
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