{"title":"基于Levenberg-Marquardt反向传播算法的自动实时视频质量测量","authors":"P. Archana, S. Kulkarni","doi":"10.1109/ICSIP.2014.61","DOIUrl":null,"url":null,"abstract":"With high volumes of Multimedia data transmission becoming a reality, most of the networks are capable of meeting only bare requirements of video information transfer. An important aspect of video transfer is to deal with User's satisfaction in terms of quality of service. Hence new methods are being proposed to meet user's criteria. In this paper a frame work has been described based on Artificial Neural Networks for real time video quality assessment.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automated Real Time Video Quality Measurement Using Levenberg-Marquardt Backpropagation Algorithm\",\"authors\":\"P. Archana, S. Kulkarni\",\"doi\":\"10.1109/ICSIP.2014.61\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With high volumes of Multimedia data transmission becoming a reality, most of the networks are capable of meeting only bare requirements of video information transfer. An important aspect of video transfer is to deal with User's satisfaction in terms of quality of service. Hence new methods are being proposed to meet user's criteria. In this paper a frame work has been described based on Artificial Neural Networks for real time video quality assessment.\",\"PeriodicalId\":111591,\"journal\":{\"name\":\"2014 Fifth International Conference on Signal and Image Processing\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Fifth International Conference on Signal and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSIP.2014.61\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Fifth International Conference on Signal and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIP.2014.61","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Real Time Video Quality Measurement Using Levenberg-Marquardt Backpropagation Algorithm
With high volumes of Multimedia data transmission becoming a reality, most of the networks are capable of meeting only bare requirements of video information transfer. An important aspect of video transfer is to deal with User's satisfaction in terms of quality of service. Hence new methods are being proposed to meet user's criteria. In this paper a frame work has been described based on Artificial Neural Networks for real time video quality assessment.