Abstract
In this paper, we propose a general-purpose no-reference (NR) video quality assessment (VQA) metric based on the cascade combination of 2D convolutional neural network (CNN), multi-layer perceptron (MLP), and support vector regression (SVR) model. The features are extracted from both spatial and spatiotemporal domains by using a 2D CNN. These features can capture different aspects of video frames for predicting quality scores, and we take these features as inputs of MLP to obtain a few estimated quality scores on different perspectives. Finally, these estimated scores are combined as a final quality score by an SVR model. The proposed method is evaluated on the well-known LIVE Video database with other state-of-the-art and well-performing VQA metrics. And the experimental result demonstrates that our method is competitive with other full-reference and NR VQA metrics.
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