{"title":"A gray-based block-matching algorithm and its VLSI architecture","authors":"Yeu-Horng Shiau, Pei-Yin Chen, J. Jou","doi":"10.1109/SIPS.1999.822310","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an efficient gray-based block-matching algorithm (GBMA) and its VLSI architecture. Based on the gray system theory, the GBMA can determine the better motion vectors of image blocks quickly. The experimental results show that the proposed algorithm performs better than other search algorithms, such as TSS, CS, PHODS, FSS, and SES, in terms of four different measures: 1) average MSE per pixel, 2) average PSNR, 3) average prediction errors per pixel, and 4) average search points per frame. The VLSI architecture of the algorithm has been designed and implemented, and it can yield a search rate of 680 K blocks/sec with a clock rate of 66 MHz.","PeriodicalId":275030,"journal":{"name":"1999 IEEE Workshop on Signal Processing Systems. SiPS 99. Design and Implementation (Cat. No.99TH8461)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 IEEE Workshop on Signal Processing Systems. SiPS 99. Design and Implementation (Cat. No.99TH8461)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIPS.1999.822310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose an efficient gray-based block-matching algorithm (GBMA) and its VLSI architecture. Based on the gray system theory, the GBMA can determine the better motion vectors of image blocks quickly. The experimental results show that the proposed algorithm performs better than other search algorithms, such as TSS, CS, PHODS, FSS, and SES, in terms of four different measures: 1) average MSE per pixel, 2) average PSNR, 3) average prediction errors per pixel, and 4) average search points per frame. The VLSI architecture of the algorithm has been designed and implemented, and it can yield a search rate of 680 K blocks/sec with a clock rate of 66 MHz.