no code implementations • 29 Aug 2023 • Yazhou Xing, Amrita Mazumdar, Anjul Patney, Chao Liu, Hongxu Yin, Qifeng Chen, Jan Kautz, Iuri Frosio
We present a learning-based system to reduce these artifacts without resorting to complex acquisition mechanisms like alternating exposures or costly processing that are typical of high dynamic range (HDR) imaging.
no code implementations • 24 Jun 2021 • Yize Jin, Anjul Patney, Richard Webb, Alan Bovik
Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain.
no code implementations • 12 Jun 2021 • Yize Jin, Anjul Patney, Alan Bovik
To reduce stresses on bandwidth, foveated video compression is regaining popularity, whereby the space-variant spatial resolution of the retina is exploited.
1 code implementation • ACM Transactions on Graphics 2021 • Rafał K. Mantiuk, Gyorgy Denes, ALEXANDRE CHAPIRO, Anton Kaplanyan, GIZEM RUFO, ROMAIN BACHY, Trisha Lian, Anjul Patney
FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception.
Ranked #20 on Video Quality Assessment on MSU FR VQA Database
1 code implementation • 29 Sep 2020 • Meixu Chen, Todd Goodall, Anjul Patney, Alan C. Bovik
Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network.
no code implementations • ICML 2020 • Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
no code implementations • 25 Sep 2019 • Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debhath, Anjul Patney, Ankit B. Patel, Anima Anandkumar
Generative adversarial networks (GANs) have achieved great success at generating realistic samples.
1 code implementation • 5 Aug 2014 • Scott A. Mitchell, Mohamed S. Ebeida, Muhammad A. Awad, Chonhyon Park, Anjul Patney, Ahmad A. Rushdi, Laura P. Swiler, Dinesh Manocha, Li-Yi Wei
Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them.
Graphics