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http://dx.doi.org/10.9717/kmms.2020.23.12.1447

No-Reference Sports Video-Quality Assessment Using 3D Shearlet Transform and Deep Residual Neural Network  

Lee, Gi Yong (Dept of Electronics Convergence, Kwangwoon University)
Shin, Seung-Su (Dept of Electronics Convergence, Kwangwoon University)
Kim, Hyoung-Gook (Dept of Electronics Convergence, Kwangwoon University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.12, 2020 , pp. 1447-1453 More about this Journal
Abstract
In this paper, we propose a method for no-reference quality assessment of sports videos using 3D shearlet transform and deep residual neural networks. In the proposed method, 3D shearlet transform-based spatiotemporal features are extracted from the overlapped video blocks and applied to logistic regression concatenated with a deep residual neural network based on a conditional video block-wise constraint to learn the spatiotemporal correlation and predict the quality score. Our evaluation reveals that the proposed method predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.
Keywords
No-Reference Video Quality Assessment; 3D Shearlet Transform; Natural Scene Statistics; Deep Residual Neural Network; Conditional Constraints;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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