{"title":"基于矢量量化的有损压缩方法","authors":"","doi":"10.25236/ajcis.2023.061001","DOIUrl":null,"url":null,"abstract":"Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lossy Compression Approaches Based on Vector Quantization\",\"authors\":\"\",\"doi\":\"10.25236/ajcis.2023.061001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.\",\"PeriodicalId\":387664,\"journal\":{\"name\":\"Academic Journal of Computing & Information Science\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Journal of Computing & Information Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25236/ajcis.2023.061001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Computing & Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25236/ajcis.2023.061001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lossy Compression Approaches Based on Vector Quantization
Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.