Deep video quality assessment using constrained multi-task regression and Spatio-temporal feature fusion-Reference-Cited by-同舟云学术

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Deep video quality assessment using constrained multi-task regression and Spatio-temporal feature fusion

Published:2023-02-15 Issue:18 Volume:82 Page:28067-28086
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Wen Mingyang,Liu Lixiong^ORCID,Sang Qingbing,Zhang Yongmei

Funder

National Natural Science Foundation of China

National Key Research and Development Program Project of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-14652-2.pdf

Reference52 articles.

1. Bampis CG, Gupta P, Soundararajan R, Bovik AC (2017) SpEED-QA: spatial efficient entropic differencing for image and video quality. IEEE Signal Process Lett 24(9):1333–1337

2. Bosse S, Maniry D, Muller KR, Wiegand T, Samek W (2018) Deep neural networks or no-reference and full-reference image quality assessment. IEEE Trans Image Process 27(1):206–219

3. Bromley J, Guyon I, LeCun Y et al (1993) Signature verification using a Siamese time delay neural network. Adv Neural Inf Proces Syst 6:737–744

4. Chen P, Li L, Ma L et al (2020) Rirnet: recurrent-in-recurrent network for video quality assessment. In: Proceedings of the 28th ACM international conference on multimedia, pp 834-842

5. Deng J, Dong W, Socher R et al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248–255

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