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In recent years, deep learning has achieved promising success for multimediaquality assessment, especially for image quality assessment (IQA). However,since there exist more complex temporal characteristics in videos, very littlework has been done on video quality assessment (VQA) by exploiting powerfuldeep convolutional neural networks (DCNNs). In this paper, we propose anefficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ)to predict the perceptual quality of various distorted videos in a no-referencemanner. In the proposed DeepSTQ, we first extract local and globalspatiotemporal features by pre-trained deep learning models without fine-tuningor training from scratch. The composited features consider distorted videoframes as well as frame difference maps from both global and local views. Then,the feature aggregation is conducted by the regression model to predict theperceptual video quality. Finally, experimental results demonstrate that ourproposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.

Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment