Efficient Subjective Video Quality Assessment Based on Active Learning and Clustering-Reference-Cited by-同舟云学术

Efficient Subjective Video Quality Assessment Based on Active Learning and Clustering

Published:2022 Issue: Volume: Page:47-56
ISSN:0302-9743
Container-title:Advances in Mobile Computing and Multimedia Intelligence
language:
Short-container-title:

Author:

Liu Xiaochen^ORCID,Song Wei^ORCID,Zhang Wenbo^ORCID,Di Mauro Mario^ORCID,Liotta Antonio^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-20436-4_5

Reference20 articles.

1. ITU-T, Recommendation P.913. https://www.itu.int/rec/T-REC-P.913-202106-I/en. Accessed 13 June 2021

2. Smailagic, A., Costa, P., Gaudio, A., et al.: O-MedAL: Online active deep learning for medical image analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 10(4), e1353 (2020)

3. Hou, G., Li, Y., Yang, H., et al.: UID2021: an underwater image dataset for evaluation of no-reference quality assessment metrics. arXiv preprint arXiv:2204.08813 (2022)

4. Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19(6), 1427–1441 (2010)

5. Sinno, Z., Bovik, A.C.: Large-scale study of perceptual video quality. IEEE Trans. Image Process. 28(2), 612–627 (2019)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective;JMIR mHealth and uHealth;2024-05-06

2. Motion Tracking of Daily Living and Physical Activities in Healthcare: A Systematic Review from Designers’ Perspective (Preprint);2023-02-07