In this work, we propose a novel no-reference (NR) video quality metric thatevaluates the impact of frame freezing due to either packet loss or latearrival. Our metric uses a trained neural network acting on features that arechosen to capture the impact of frame freezing on the perceived quality. Theconsidered features include the number of freezes, freeze duration statistics,inter-freeze distance statistics, frame difference before and after the freeze,normal frame difference, and the ratio of them. We use the neural network tofind the mapping between features and subjective test scores. We optimize thenetwork structure and the feature selection through a cross validationprocedure, using training samples extracted from both VQEG and LIVE videodatabases. The resulting feature set and network structure yields accuratequality prediction for both the training data containing 54 test videos and aseparate testing dataset including 14 videos, with Pearson CorrelationCoefficients greater than 0.9 and 0.8 for the training set and the testing set,respectively. Our proposed metric has low complexity and could be utilized in asystem with realtime processing constraint.
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