L. Raja, R. Swaminathan, D. K. Sharma, R. Regin, S. R, D. Babu
{"title":"基于运动矢量分类的视频编码残差信号传输优化","authors":"L. Raja, R. Swaminathan, D. K. Sharma, R. Regin, S. R, D. Babu","doi":"10.1109/I-SMAC52330.2021.9640930","DOIUrl":null,"url":null,"abstract":"Figuring interpolated images is an exemplary issue in picture and video preparation. It is an essential advancement in various applications, such as frame rate conversion, temporal upsampling for producing moderate movement video, and picture transforming, just as virtual view blend. Standard ways to deal with figuring interpolated frames in a video grouping require precise pixel correspondences between pictures. Customary answers for picture interjection initially register correspondences (for the most part utilizing optical stream or stereo techniques), trailed by correspondence-based picture distorting. Because of characteristic ambiguities in figuring such correspondences, most strategies are vigorously subject to computationally costly worldwide improvement and require extensive parameter tuning. In this proposed technique, a new complication motion with a low vector dispensation procedure at the end side is suggested for motion-compensated video vector frame interpolation or frame rate up-conversion. Generally, it is possible to detect the problems of broken edges and distorted arrangement difficulties in a frame interpolation through orderly refining motion vectors on various block sizes. Investigational consequences show that this proposed organization’s visual superiority is improved and is also rugged, even in video sequences containing fast motions and difficult sections.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimize the Residual Signal Transmission Based on Moving Vector Classification for Video Coding\",\"authors\":\"L. Raja, R. Swaminathan, D. K. Sharma, R. Regin, S. R, D. Babu\",\"doi\":\"10.1109/I-SMAC52330.2021.9640930\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Figuring interpolated images is an exemplary issue in picture and video preparation. It is an essential advancement in various applications, such as frame rate conversion, temporal upsampling for producing moderate movement video, and picture transforming, just as virtual view blend. Standard ways to deal with figuring interpolated frames in a video grouping require precise pixel correspondences between pictures. Customary answers for picture interjection initially register correspondences (for the most part utilizing optical stream or stereo techniques), trailed by correspondence-based picture distorting. Because of characteristic ambiguities in figuring such correspondences, most strategies are vigorously subject to computationally costly worldwide improvement and require extensive parameter tuning. In this proposed technique, a new complication motion with a low vector dispensation procedure at the end side is suggested for motion-compensated video vector frame interpolation or frame rate up-conversion. Generally, it is possible to detect the problems of broken edges and distorted arrangement difficulties in a frame interpolation through orderly refining motion vectors on various block sizes. Investigational consequences show that this proposed organization’s visual superiority is improved and is also rugged, even in video sequences containing fast motions and difficult sections.\",\"PeriodicalId\":178783,\"journal\":{\"name\":\"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I-SMAC52330.2021.9640930\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC52330.2021.9640930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimize the Residual Signal Transmission Based on Moving Vector Classification for Video Coding
Figuring interpolated images is an exemplary issue in picture and video preparation. It is an essential advancement in various applications, such as frame rate conversion, temporal upsampling for producing moderate movement video, and picture transforming, just as virtual view blend. Standard ways to deal with figuring interpolated frames in a video grouping require precise pixel correspondences between pictures. Customary answers for picture interjection initially register correspondences (for the most part utilizing optical stream or stereo techniques), trailed by correspondence-based picture distorting. Because of characteristic ambiguities in figuring such correspondences, most strategies are vigorously subject to computationally costly worldwide improvement and require extensive parameter tuning. In this proposed technique, a new complication motion with a low vector dispensation procedure at the end side is suggested for motion-compensated video vector frame interpolation or frame rate up-conversion. Generally, it is possible to detect the problems of broken edges and distorted arrangement difficulties in a frame interpolation through orderly refining motion vectors on various block sizes. Investigational consequences show that this proposed organization’s visual superiority is improved and is also rugged, even in video sequences containing fast motions and difficult sections.