Deep ensembling for perceptual image quality assessment
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-021-06662-9.pdf
Reference48 articles.
1. Ahmed N, Asif HMS (2019) Ensembling convolutional neural networks for perceptual image quality assessment. In: 2019 13th international conference on mathematics, actuarial science, computer science and statistics (MACS). IEEE
2. Ahmed N, Asif HMS (2020) Perceptual quality assessment of digital images using deep features. Comput Inform 39(3):385–409
3. Ahmed N, Asif HMS, Khalid H (2019) Image quality assessment using a combination of hand-crafted and deep features. In: International conference on intelligent technologies and applications. Springer
4. Ahmed N, Asif HMS, Khalid H (2021) PIQI: perceptual image quality index based on ensemble of Gaussian process regression. Multimed Tools Appl. p. 1–24
5. Bare B, Li K, Yan B (2017) An accurate deep convolutional neural networks model for no-reference image quality assessment. In: 2017 IEEE international conference on multimedia and expo (ICME). IEEE
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