Open Access
Issue
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
Article Number 01034
Number of page(s) 6
Section Computer Technology and System Design
DOI https://doi.org/10.1051/itmconf/20224501034
Published online 19 May 2022
  1. B. Zoph and Q.V. Le. Neural architecture search with reinforcement learning. ICLR, (2017). [Google Scholar]
  2. B. Zoph, V. Vasudevan, J. Shlens, and Q.V. Le. Learning transferable architectures for scalable image recognition. CVPR, (2018). [Google Scholar]
  3. H.X. Liu, K. Simonyan, Y.M. Yang. Differentiable neural network architecture search. ICLR (2019) [Google Scholar]
  4. H. Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. Efficient neural architecture search via parameter sharing ICML, (2018) b. [Google Scholar]
  5. A. K. Moorthy and A. C. Bovik, Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Trans Image Process, vol. 20, no. 12, pp. 3350–3364, Dec. (2011). [CrossRef] [MathSciNet] [Google Scholar]
  6. W. Hou, X. Gao, D. Tao, et al. Blind image quality assessment via deep learning. IEEE Trans Neural Netw Learn Syst, (2015), 26(6):1275-1286. [CrossRef] [MathSciNet] [Google Scholar]
  7. L. He, D. Tao, X. Li, et al. Sparse representation for blind image quality assessment. Proceedings of 2012 IEEE CVPR, (2012):1146-1153. [Google Scholar]
  8. K. Ma, W. Liu, T. Liu, et al. dipIQ:Blind image quality assessment by learningtorank discriminable image pairs[J]. IEEE Trans Image Process, (2017), 26(8):3951-3964 [CrossRef] [MathSciNet] [Google Scholar]
  9. P. Ye, J. Kumar, L. Kang, et al. Unsupervised feature learning framework for noreference image quality assessment[C]. IEEE CVPR (2012): 1098-1105. [Google Scholar]
  10. P. Ye, J. Kumar, D. Doermann. Beyond human opinion scores: Blind image quality assessment based on synthetic scores[C]. IEEE CVPR, (2014): 4241-4248. [Google Scholar]
  11. A. Mittal, A.K. Moorthy, A.C. Bovik. No-reference image quality assessment in the spatial domain[J] IEEE Trans Image Process, (2012), 21(12):4695-4708. [CrossRef] [MathSciNet] [Google Scholar]
  12. L. Zhang, L. Zhang, A.C. Bovik. A feature-enriched completely blind image quality evaluator[J]. IEEE Trans Image Process, (2015), 24(8):2579-2591. [CrossRef] [MathSciNet] [Google Scholar]
  13. A.K. Moorthy, A.C. Bovik. Blind image quality assessment: From natural scene statistics to perceptual quality [J] IEEE Trans Image Process, (2011), 20(12): 3350-3364. [CrossRef] [MathSciNet] [Google Scholar]
  14. A.K. Moorthy, A.C. Bovik. A two-step framework for constructing blind image quality indices [J]. IEEE Signal Process Lett, (2010), 17(5):513-516 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.