{"title":"基于神经网络的无参考视频质量体验度量","authors":"Amal Sufiuh Ajrash, R. F. Ghani, L. Al-Jobouri","doi":"10.1109/CEEC47804.2019.8974336","DOIUrl":null,"url":null,"abstract":"In recent years, there has been a tremendous development in information technology, which has led to the possibility of fast access to different data types over the Internet, one of the data type is video and the ability for the user to watch video directly online. This paper provides a video streaming QoE evaluation metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that are used to train the neural network and finally evaluate the QoE value. Verify training models prediction using 10-fold cross-validation. The proposed system had the best correlation result 0.95 in SRCC metric.","PeriodicalId":331160,"journal":{"name":"2019 11th Computer Science and Electronic Engineering (CEEC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"ANN based Measurement for No-Reference Video Quality of Experience Metric\",\"authors\":\"Amal Sufiuh Ajrash, R. F. Ghani, L. Al-Jobouri\",\"doi\":\"10.1109/CEEC47804.2019.8974336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, there has been a tremendous development in information technology, which has led to the possibility of fast access to different data types over the Internet, one of the data type is video and the ability for the user to watch video directly online. This paper provides a video streaming QoE evaluation metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that are used to train the neural network and finally evaluate the QoE value. Verify training models prediction using 10-fold cross-validation. The proposed system had the best correlation result 0.95 in SRCC metric.\",\"PeriodicalId\":331160,\"journal\":{\"name\":\"2019 11th Computer Science and Electronic Engineering (CEEC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 11th Computer Science and Electronic Engineering (CEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEEC47804.2019.8974336\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th Computer Science and Electronic Engineering (CEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEC47804.2019.8974336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ANN based Measurement for No-Reference Video Quality of Experience Metric
In recent years, there has been a tremendous development in information technology, which has led to the possibility of fast access to different data types over the Internet, one of the data type is video and the ability for the user to watch video directly online. This paper provides a video streaming QoE evaluation metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that are used to train the neural network and finally evaluate the QoE value. Verify training models prediction using 10-fold cross-validation. The proposed system had the best correlation result 0.95 in SRCC metric.