{"title":"基于H.264/AVC码流的质量评价指标研究","authors":"Zhiyuan Shi, Pingbo Chen, Chao Feng, Lianfeng Huang, Weijian Xu","doi":"10.1109/ICASID.2012.6325335","DOIUrl":null,"url":null,"abstract":"No-reference(NR) video quality metrics are more practical in real-time applications compared to full-reference(FR) metrics. This contribution proposed a No-reference video quality assessment metric based on H.264/AVC bitstream through extracting features from the H.264/AVC encoded bitstream. After the extraction of the features which are very important for video quality assessment, we use Partial Least Squares Regression(PLSR) to calculate the weights of them. Then a quality prediction model is also proposed. During the experiments, the results show that our NR metric has low computing complexity. Finally, compared to subjective assessment, we find that there is a high correlation between quality prediction and the actual quality of 0.95.","PeriodicalId":408223,"journal":{"name":"Anti-counterfeiting, Security, and Identification","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Research on quality assessment metric based on H.264/AVC bitstream\",\"authors\":\"Zhiyuan Shi, Pingbo Chen, Chao Feng, Lianfeng Huang, Weijian Xu\",\"doi\":\"10.1109/ICASID.2012.6325335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"No-reference(NR) video quality metrics are more practical in real-time applications compared to full-reference(FR) metrics. This contribution proposed a No-reference video quality assessment metric based on H.264/AVC bitstream through extracting features from the H.264/AVC encoded bitstream. After the extraction of the features which are very important for video quality assessment, we use Partial Least Squares Regression(PLSR) to calculate the weights of them. Then a quality prediction model is also proposed. During the experiments, the results show that our NR metric has low computing complexity. Finally, compared to subjective assessment, we find that there is a high correlation between quality prediction and the actual quality of 0.95.\",\"PeriodicalId\":408223,\"journal\":{\"name\":\"Anti-counterfeiting, Security, and Identification\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anti-counterfeiting, Security, and Identification\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASID.2012.6325335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anti-counterfeiting, Security, and Identification","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASID.2012.6325335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on quality assessment metric based on H.264/AVC bitstream
No-reference(NR) video quality metrics are more practical in real-time applications compared to full-reference(FR) metrics. This contribution proposed a No-reference video quality assessment metric based on H.264/AVC bitstream through extracting features from the H.264/AVC encoded bitstream. After the extraction of the features which are very important for video quality assessment, we use Partial Least Squares Regression(PLSR) to calculate the weights of them. Then a quality prediction model is also proposed. During the experiments, the results show that our NR metric has low computing complexity. Finally, compared to subjective assessment, we find that there is a high correlation between quality prediction and the actual quality of 0.95.