{"title":"A Novel Motion Compensated Frame Interpolation Based on Block-Merging and Residual Energy","authors":"Ai-Mei Huang, Truong Q. Nguyen","doi":"10.1109/MMSP.2006.285338","DOIUrl":null,"url":null,"abstract":"In this paper, a novel motion compensated frame interpolation (MCFI) algorithm by merging blocks that have unreliable motion vectors (MVs) based on their residual errors is proposed. Unlike the conventional methods that find true motion using smaller blocks and vector median filter, we proposed to find one single motion vector to represent a group of adjacent macroblocks (MBs) where the conventional MCFI methods are likely to fail, likely to fail. The proposed method is able to preserve the structure of different objects and their edge information, without requiring complicated edge detection and object-based motion estimation. Experimental results show that the proposed scheme improves both visual quality and PSNR, especially in the areas with different motions and the motion boundary","PeriodicalId":267577,"journal":{"name":"2006 IEEE Workshop on Multimedia Signal Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Workshop on Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2006.285338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
In this paper, a novel motion compensated frame interpolation (MCFI) algorithm by merging blocks that have unreliable motion vectors (MVs) based on their residual errors is proposed. Unlike the conventional methods that find true motion using smaller blocks and vector median filter, we proposed to find one single motion vector to represent a group of adjacent macroblocks (MBs) where the conventional MCFI methods are likely to fail, likely to fail. The proposed method is able to preserve the structure of different objects and their edge information, without requiring complicated edge detection and object-based motion estimation. Experimental results show that the proposed scheme improves both visual quality and PSNR, especially in the areas with different motions and the motion boundary