Abstract
AbstractDepending on different contents of video sequences, the compression settings also differ. So many parameters can be obtained from video steams. We extract and analyze a large set of parameters from different layer. Based on the correlation between features and the subjective perceived quality, the most important objective parameters are picked out. A low complexity objective quality assessment metric is obtained by a linear calculation on the selected parameters. The presented method can perform continuous objective quality assessment in unit of GOP (Group of Pictures). The experimental results show that our model can achieve good performance for video quality prediction. In addition, our model does not require the source, or the decoded picture, it is suitable for real-time applications. And continuous quality assessment can provide an automatic warning without delay when picture quality problems occur.KeywordsVideo QualityMean Opinion ScoreImage Quality AssessmentCompression SettingVideo Quality AssessmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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