Open AccessNo‐reference quality assessment of dynamic sports videos based on a spatiotemporal motion model
Author(s) -
Kim HyoungGook,
Shin SeungSu,
Kim SangWook,
Lee Gi Yong
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0160
Subject(s) - computer science , artificial intelligence , video quality , subjective video quality , residual , computer vision , motion (physics) , concatenation (mathematics) , block (permutation group theory) , motion compensation , quality (philosophy) , block matching algorithm , pattern recognition (psychology) , video processing , image quality , video tracking , image (mathematics) , algorithm , mathematics , engineering , metric (unit) , philosophy , operations management , geometry , epistemology , combinatorics
This paper proposes an approach to improve the performance of no‐reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block‐wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no‐reference video quality assessment methods.