{"title":"SRQM:一种空间分辨率适应的视频质量度量","authors":"Alex Mackin, Mariana Afonso, Fan Zhang, D. Bull","doi":"10.1109/PCS.2018.8456246","DOIUrl":null,"url":null,"abstract":"This paper presents a full reference objective video quality metric (SRQM), which characterises the relationship between variations in spatial resolution and visual quality in the context of adaptive video formats. SRQM uses wavelet decomposition, subband combination with perceptually inspired weights, and spatial pooling, to estimate the relative quality between the frames of a high resolution reference video, and one that has been spatially adapted through a combination of down and upsampling. The uVI-SR video database is used to benchmark SRQM against five commonly-used quality metrics. The database contains 24 diverse video sequences that span a range of spatial resolutions up to UHD-I $(3840\\times 2160)$. An in- depth analysis demonstrates that SRQM is statistically superior to the other quality metrics for all tested adaptation filters, and all with relatively low computational complexity.","PeriodicalId":433667,"journal":{"name":"2018 Picture Coding Symposium (PCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"SRQM: A Video Quality Metric for Spatial Resolution Adaptation\",\"authors\":\"Alex Mackin, Mariana Afonso, Fan Zhang, D. Bull\",\"doi\":\"10.1109/PCS.2018.8456246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a full reference objective video quality metric (SRQM), which characterises the relationship between variations in spatial resolution and visual quality in the context of adaptive video formats. SRQM uses wavelet decomposition, subband combination with perceptually inspired weights, and spatial pooling, to estimate the relative quality between the frames of a high resolution reference video, and one that has been spatially adapted through a combination of down and upsampling. The uVI-SR video database is used to benchmark SRQM against five commonly-used quality metrics. The database contains 24 diverse video sequences that span a range of spatial resolutions up to UHD-I $(3840\\\\times 2160)$. An in- depth analysis demonstrates that SRQM is statistically superior to the other quality metrics for all tested adaptation filters, and all with relatively low computational complexity.\",\"PeriodicalId\":433667,\"journal\":{\"name\":\"2018 Picture Coding Symposium (PCS)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Picture Coding Symposium (PCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PCS.2018.8456246\",\"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 Picture Coding Symposium (PCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCS.2018.8456246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SRQM: A Video Quality Metric for Spatial Resolution Adaptation
This paper presents a full reference objective video quality metric (SRQM), which characterises the relationship between variations in spatial resolution and visual quality in the context of adaptive video formats. SRQM uses wavelet decomposition, subband combination with perceptually inspired weights, and spatial pooling, to estimate the relative quality between the frames of a high resolution reference video, and one that has been spatially adapted through a combination of down and upsampling. The uVI-SR video database is used to benchmark SRQM against five commonly-used quality metrics. The database contains 24 diverse video sequences that span a range of spatial resolutions up to UHD-I $(3840\times 2160)$. An in- depth analysis demonstrates that SRQM is statistically superior to the other quality metrics for all tested adaptation filters, and all with relatively low computational complexity.
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