Abstract-In this work, we propose a novel no-reference (NR) video quality metric that evaluates the impact of frame freezing due to either packet loss or late arrival. Our metric uses a trained neural network acting on features that are chosen to capture the impact of frame freezing on the perceived quality. The considered 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 to find the mapping between features and subjective test scores. We optimize the network structure and the feature selection through a cross validation procedure, using training samples extracted from both VQEG and LIVE video databases. The resulting feature set and network structure yields accurate quality prediction for both the training data containing 54 test videos and a separate testing dataset including 14 videos, with Pearson Correlation Coefficients 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 a system with realtime processing constraint. Index Terms-video quality metric, neural network, packet loss, temporal jerkiness.
Xue et al. in REF introduced a NR ANN-based video quality metric to predict the quality by considering the impact of frame freezing due to packet loss and/or late arrival.
A Novel No-reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing
ArXiv
ArXiv
Computer Science