Abstract:
A reliable video quality assessment (VQA) algorithm is essential for evaluating and optimizing video processing pipelines. In this paper, we propose a quality aggregation...Show MoreMetadata
Abstract:
A reliable video quality assessment (VQA) algorithm is essential for evaluating and optimizing video processing pipelines. In this paper, we propose a quality aggregation network (QAN) for full-reference VQA, which models the characteristics of human visual perception of video quality in both spatial and temporal domain. The proposed QAN is composed of two mod-ules, the spatial quality aggregation (SQA) network and the tem-poral quality aggregation (TQA) network. Specifically, the SQA network models the quality of video frames using 3D CNN, taking both spatial and temporal masking effects into consideration for the modeling of the perception of human visual system (HVS). In the TQA network, considering the memory effect of HVS facing the temporal variation of frame-level quality, an LSTM-based temporal quality pooling network is proposed to capture the nonlinearities and temporal dependencies involved in the process of quality evaluation. According to the experimental results on two well-established VQA databases, the proposed model could outperform the state-of-the-art metrics. The code of the proposed method is available at: https://github.com/lorenzowu/QAN.
Published in: 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Date of Conference: 13-16 December 2022
Date Added to IEEE Xplore: 16 January 2023
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- IEEE Keywords
- Index Terms
- Quality Assessment ,
- Video Quality ,
- Aggregation Network ,
- Video Quality Assessment ,
- Convolutional Neural Network ,
- Visual Perception ,
- Temporal Features ,
- Memory Effect ,
- Temporal Domain ,
- Human Vision ,
- Masking Effect ,
- Human Visual System ,
- Spatial Aggregation ,
- Temporal Aggregation ,
- Loss Function ,
- Contralateral ,
- Neural Network ,
- Learning Rate ,
- Wireless ,
- Convolutional Layers ,
- Total Variation Regularization ,
- Convolutional Long Short-term Memory ,
- Error Map ,
- Convolution Operation ,
- Video Clips ,
- Spatiotemporal Characteristics ,
- Adaptive Moment Estimation Optimizer ,
- Peak Signal-to-noise Ratio ,
- Feature Maps ,
- Target Frame
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Quality Assessment ,
- Video Quality ,
- Aggregation Network ,
- Video Quality Assessment ,
- Convolutional Neural Network ,
- Visual Perception ,
- Temporal Features ,
- Memory Effect ,
- Temporal Domain ,
- Human Vision ,
- Masking Effect ,
- Human Visual System ,
- Spatial Aggregation ,
- Temporal Aggregation ,
- Loss Function ,
- Contralateral ,
- Neural Network ,
- Learning Rate ,
- Wireless ,
- Convolutional Layers ,
- Total Variation Regularization ,
- Convolutional Long Short-term Memory ,
- Error Map ,
- Convolution Operation ,
- Video Clips ,
- Spatiotemporal Characteristics ,
- Adaptive Moment Estimation Optimizer ,
- Peak Signal-to-noise Ratio ,
- Feature Maps ,
- Target Frame
- Author Keywords