M. Fujibayashi, T. Nozawa, T. Nakayama, K. Mochizuki, M. Konda, K. Kotani, S. Sugawa, T. Ohmi
{"title":"一种基于自适应分辨率矢量量化的静态图像编码器,压缩比超过1/200,具有不必要的计算消除结构","authors":"M. Fujibayashi, T. Nozawa, T. Nakayama, K. Mochizuki, M. Konda, K. Kotani, S. Sugawa, T. Ohmi","doi":"10.1109/VLSIC.2002.1015099","DOIUrl":null,"url":null,"abstract":"We have developed an advanced vector quantization (VQ) encoding hardware for still image encoding systems. By utilizing needless calculation elimination method, computational cost of VQ encoding is reduced to 40% or less, while maintaining the accuracy of full-search VQ. We have also developed a still image compression algorithm based on adaptive resolution VQ (AR-VQ), which realizes compression ratio over 1/200 while maintaining image quality. We have successfully implemented these two technologies into a still image encoding processor. The processor can compress still image of 1600/spl times/2400 pixels within one second, which is 60 times faster than software implementation on current PCs.","PeriodicalId":162493,"journal":{"name":"2002 Symposium on VLSI Circuits. Digest of Technical Papers (Cat. No.02CH37302)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A still image encoder based on adaptive resolution vector quantization realizing compression ratio over 1/200 featuring needless calculation elimination architecture\",\"authors\":\"M. Fujibayashi, T. Nozawa, T. Nakayama, K. Mochizuki, M. Konda, K. Kotani, S. Sugawa, T. Ohmi\",\"doi\":\"10.1109/VLSIC.2002.1015099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have developed an advanced vector quantization (VQ) encoding hardware for still image encoding systems. By utilizing needless calculation elimination method, computational cost of VQ encoding is reduced to 40% or less, while maintaining the accuracy of full-search VQ. We have also developed a still image compression algorithm based on adaptive resolution VQ (AR-VQ), which realizes compression ratio over 1/200 while maintaining image quality. We have successfully implemented these two technologies into a still image encoding processor. The processor can compress still image of 1600/spl times/2400 pixels within one second, which is 60 times faster than software implementation on current PCs.\",\"PeriodicalId\":162493,\"journal\":{\"name\":\"2002 Symposium on VLSI Circuits. Digest of Technical Papers (Cat. No.02CH37302)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2002 Symposium on VLSI Circuits. Digest of Technical Papers (Cat. No.02CH37302)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSIC.2002.1015099\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2002 Symposium on VLSI Circuits. Digest of Technical Papers (Cat. No.02CH37302)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIC.2002.1015099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A still image encoder based on adaptive resolution vector quantization realizing compression ratio over 1/200 featuring needless calculation elimination architecture
We have developed an advanced vector quantization (VQ) encoding hardware for still image encoding systems. By utilizing needless calculation elimination method, computational cost of VQ encoding is reduced to 40% or less, while maintaining the accuracy of full-search VQ. We have also developed a still image compression algorithm based on adaptive resolution VQ (AR-VQ), which realizes compression ratio over 1/200 while maintaining image quality. We have successfully implemented these two technologies into a still image encoding processor. The processor can compress still image of 1600/spl times/2400 pixels within one second, which is 60 times faster than software implementation on current PCs.