{"title":"视频无参考在线容错测试研究","authors":"Tong-Yu Hsieh, Shang-En Chan, Chi-Hsuan Ho","doi":"10.1109/ETS.2018.8400711","DOIUrl":null,"url":null,"abstract":"In this paper we investigate how to achieve on-line error-tolerability testing on videos. In particular, a no-reference manner is considered, which means that no reference videos are needed for comparison with the videos under test. As a result, the hardware that is usually needed in conventional on-line test methods for generating reference data can be totally eliminated. This greatly reduces implementation complexity of on-line test procedures. We show that by well exploiting some simple attributes, the acceptability for 81,412 various erroneous videos can be accurately determined with more than 90% accuracy. As a comparison, the related previous work can only achieve about 80% accuracy. In addition, our attribute acquirement process requires only 33% of the computation time for the previous work.","PeriodicalId":223459,"journal":{"name":"2018 IEEE 23rd European Test Symposium (ETS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"On no-reference on-line error-tolerability testing for videos\",\"authors\":\"Tong-Yu Hsieh, Shang-En Chan, Chi-Hsuan Ho\",\"doi\":\"10.1109/ETS.2018.8400711\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we investigate how to achieve on-line error-tolerability testing on videos. In particular, a no-reference manner is considered, which means that no reference videos are needed for comparison with the videos under test. As a result, the hardware that is usually needed in conventional on-line test methods for generating reference data can be totally eliminated. This greatly reduces implementation complexity of on-line test procedures. We show that by well exploiting some simple attributes, the acceptability for 81,412 various erroneous videos can be accurately determined with more than 90% accuracy. As a comparison, the related previous work can only achieve about 80% accuracy. In addition, our attribute acquirement process requires only 33% of the computation time for the previous work.\",\"PeriodicalId\":223459,\"journal\":{\"name\":\"2018 IEEE 23rd European Test Symposium (ETS)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 23rd European Test Symposium (ETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETS.2018.8400711\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd European Test Symposium (ETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETS.2018.8400711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On no-reference on-line error-tolerability testing for videos
In this paper we investigate how to achieve on-line error-tolerability testing on videos. In particular, a no-reference manner is considered, which means that no reference videos are needed for comparison with the videos under test. As a result, the hardware that is usually needed in conventional on-line test methods for generating reference data can be totally eliminated. This greatly reduces implementation complexity of on-line test procedures. We show that by well exploiting some simple attributes, the acceptability for 81,412 various erroneous videos can be accurately determined with more than 90% accuracy. As a comparison, the related previous work can only achieve about 80% accuracy. In addition, our attribute acquirement process requires only 33% of the computation time for the previous work.