Abstract

In this paper, we present a novel no-reference (NR) model for perceptual video quality assessment, which can make quality prediction for high definition (HD) videos. This model is based on an artificial neural network (ANN) implemented by the back-propagation algorithm (BP), named as BP-ANN. Six video features are extracted from temporal and spatial domains as the input vectors. Subjective assessments are carried out by using double stimulus continuous quality scales (DSCQS) as the mean opinion scores (MOS), which are desired responses to the output layer. We establish a sample database to store all the videos, feature vectors and its corresponding MOS. Due to the combination of chrome features incorporated with a good use of regions of interest (ROI), our model can achieve good performance for the video quality prediction.

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