2020/09/07 by Wei Zhou, Zhou, Wei, Zhibo Chen +1
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image and Video Quality Assessment #Multimedia (cs.MM) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.03411
openalex publication_date 2020/09/07 · openalex created_date 2020/09/14 · openalex updated_date 2026/07/28
In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.