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No-Reference Video Quality Assessment Based on Convolution and Self-Attention Combined Network and Spatial-Temporal Fusion
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Yuzhen Niu1, 2, 3), Zhenlong Wang1), Bolin Zhang1), Yuzhong Cheng1, 2, 3)
Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12) : 2170 - 2180
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Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12): 2170-2180
No-Reference Video Quality Assessment Based on Convolution and Self-Attention Combined Network and Spatial-Temporal Fusion
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Yuzhen Niu1, 2, 3), Zhenlong Wang1), Bolin Zhang1), Yuzhong Cheng1, 2, 3)
Affiliations
  • 1)College of Computer and Data Science, Fuzhou University, Fuzhou 350108
  • 2)Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350108
  • 3)Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108
Published: 2025-12-20 doi: 10.3724/SP.J.1089.2023-00808
Outline
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Sharing videos on the Internet is becoming increasingly common, therefore, video quality assessment is becoming increasingly important. In existing video quality assessment research, convolution is widely used for feature extraction in the spatial and temporal domains of video, but due to its inherent local perceptual characteristics, the global information perception for video is neglected. At the same time, existing methods do not fully integrate spatial and temporal features. To solve the above problems, this paper proposes a no-reference video quality assessment method that combines convolution with a self-attention mechanism and spatiotemporal fusion. To extract more effective spatial and temporal features, the method first designs a spatial feature extraction module and a temporal feature extraction module, where the spatial feature extraction module enhances the global information of the local features extracted by convolution through long-range dependencies by the proposed feature perception and enhancement module that combines convolution and self-attention. The temporal feature extraction module jointly learns the temporal features of video using Video Swin Transformer and 3D convolution based on video frame sequences and video frame difference sequences. Then, the spatiotemporal feature fusion and enhancement module is used to fuse and enhance the temporal and spatial features through the self-attention mechanism, and finally predict the video quality. Experimental results on the KoNVid-1k and YouTube-UGC datasets show that the proposed method has better performance compared to existing video quality assessment methods, and ablation experiments also verify the effectiveness of each proposed module.

no-reference video quality assessment  /  3D convolutional neural network  /  self-attention mechanism
牛玉贞, 王镇隆, 张博林, 陈羽中. 卷积与自注意力结合和时空融合的无参考视频质量评价. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2170 -2180 . DOI: 10.3724/SP.J.1089.2023-00808
Yuzhen Niu, Zhenlong Wang, Bolin Zhang, Yuzhong Cheng. No-Reference Video Quality Assessment Based on Convolution and Self-Attention Combined Network and Spatial-Temporal Fusion[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2170 -2180 . DOI: 10.3724/SP.J.1089.2023-00808
Year 2025 volume 37 Issue 12
73
12
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Article Info
doi: 10.3724/SP.J.1089.2023-00808
  • Receive Date:2023-12-18
  • Online Date:2026-10-10
  • Published:2025-12-20
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History
  • Received:2023-12-18
  • Revised:2024-07-15
Funding
Affiliations
    1)College of Computer and Data Science, Fuzhou University, Fuzhou 350108
    2)Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350108
    3)Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108
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表12种不同金属材料的力学参数
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Family
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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