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author:

Niu, Y. (Niu, Y..) [1] | Zheng, Y. (Zheng, Y..) [2] | Wang, Z. (Wang, Z..) [3] | Zhong, M. (Zhong, M..) [4] | Zhao, T. (Zhao, T..) [5]

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Abstract:

Blind quality assessment for user-generated content (UGC) or consumer videos is challenging in computer vision. Two open issues are yet to be addressed: how to effectively extract high-dimensional spatial-temporal features of consumer videos and how to appropriately model the relationship between these features and user perceptions within a unified blind video quality assessment (BVQA). To tackle these issues, we propose a novel BVQA model with spatial-temporal perception and fusion. Firstly, we develop two perception modules to extract the perceptual-distortion-related features separately from the spatial and temporal domains. In particular, the temporal-domain features are obtained with a combination of 3D ConvNet and residual frames for their high efficiencies in capturing the motion-specific temporal features. Secondly, we propose a feature fusion module that adaptively combines spatial-temporal features. Finally, we map the fused features onto perceptual quality. Experimental results demonstrate that our model outperforms other advanced methods in conducting subjective video quality prediction. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keyword:

Consumer video Image quality assessment User generated content Video quality assessment

Community:

  • [ 1 ] [Niu Y.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China
  • [ 2 ] [Niu Y.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Zheng Y.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China
  • [ 4 ] [Wang Z.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China
  • [ 5 ] [Zhong M.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China
  • [ 6 ] [Zhao T.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China

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Source :

Multimedia Tools and Applications

ISSN: 1380-7501

Year: 2023

Issue: 7

Volume: 83

Page: 18969-18986

3 . 0

JCR@2023

3 . 0 0 0

JCR@2023

ESI HC Threshold:32

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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