{"title":"An Effective Motion Re-estimation in Frame-Skipping Video Transcoding","authors":"V. Patil, Rajeev Kumar","doi":"10.1109/ICCTA.2007.135","DOIUrl":null,"url":null,"abstract":"A new motion vector (MV) composition method, which we call temporal bi-directional dominant vector selection (TBDVS), for frame-skipping transcoding of pre-coded video is presented. The TBDVS utilizes a existing relationship, in pre-coded video, between the pixels of a frame and its reference frame(s) via MVs and selects a dominant MV from a set of candidate MVs to minimize prediction errors. It is general enough to handle frame-skipping, including I frame, in an arbitrary manner from a generic I-B-P frame structure video. Experimental results using cascaded pixel-domain transcoder show performance gain over the existing methods in terms of both quality and bit rate. We also propose a refinement method that adaptively adjusts the search range to further improve the performance at marginal cost","PeriodicalId":308247,"journal":{"name":"2007 International Conference on Computing: Theory and Applications (ICCTA'07)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Computing: Theory and Applications (ICCTA'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCTA.2007.135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
A new motion vector (MV) composition method, which we call temporal bi-directional dominant vector selection (TBDVS), for frame-skipping transcoding of pre-coded video is presented. The TBDVS utilizes a existing relationship, in pre-coded video, between the pixels of a frame and its reference frame(s) via MVs and selects a dominant MV from a set of candidate MVs to minimize prediction errors. It is general enough to handle frame-skipping, including I frame, in an arbitrary manner from a generic I-B-P frame structure video. Experimental results using cascaded pixel-domain transcoder show performance gain over the existing methods in terms of both quality and bit rate. We also propose a refinement method that adaptively adjusts the search range to further improve the performance at marginal cost
针对预编码视频的跳帧转码问题,提出了一种新的运动矢量(MV)合成方法——时域双向优势矢量选择(TBDVS)。TBDVS通过MV利用预编码视频中帧像素与其参考帧像素之间的现有关系,并从一组候选MV中选择一个主导MV以最小化预测误差。它一般足以处理跳帧,包括I帧,以任意方式从通用的I- b - p帧结构视频。实验结果表明,使用级联像素域转码器在质量和比特率方面都优于现有方法。我们还提出了一种自适应调整搜索范围的改进方法,以进一步提高边际成本下的性能