Deep local and global spatiotemporal feature aggregation for blind video quality assessment

Zhou, Wei and Chen, Zhibo 2020. Deep local and global spatiotemporal feature aggregation for blind video quality assessment. Presented at: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), Macau, 01-04 December 2020. 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 10.1109/VCIP49819.2020.9301764

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Abstract

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.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-7281-8069-4
ISSN:	1018-8770
Last Modified:	21 Sep 2023 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/162066

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