Affiliation:
1. MPI Informatik
2. Fraunhofer IIS
3. University College London
Subject
Computer Graphics and Computer-Aided Design
Reference64 articles.
1. Image Quality Assessment by Comparing CNN Features between Images
2. Objective image quality assessment of 3D synthesized views;Battisti F.;Image Commun.,2015
3. BiancoS. CelonaL. NapoletanoP. SchettiniR.: On the use of deep learning for blind image quality assessment.arXiv:1602.05531(2016). 2
4. BergerK. LipskiC. LinzC. SellentA. MagnorM.: A ghosting artifact detector for interpolated image quality assessment. InIEEE Int. Symp. on Consumer Electronics(2010) pp.1–6. 3
5. Deep neural networks for no‐reference and full‐reference image quality assessment;Bosse S.;IEEE TIP,2018
Cited by
1 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. Frame Interpolation Transformer and Uncertainty Guidance;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06