Nick Johnston
Nick Johnston
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Cited by
Cited by
Variational image compression with a scale hyperprior
J Ballé, D Minnen, S Singh, SJ Hwang, N Johnston
arXiv preprint arXiv:1802.01436, 2018
Full resolution image compression with recurrent neural networks
G Toderici, D Vincent, N Johnston, S Jin Hwang, D Minnen, J Shor, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
Im2Calories: towards an automated mobile vision food diary
A Meyers, N Johnston, V Rathod, A Korattikara, A Gorban, N Silberman, ...
Proceedings of the IEEE International Conference on Computer Vision, 1233-1241, 2015
Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks
N Johnston, D Vincent, D Minnen, M Covell, S Singh, T Chinen, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
J Malmaud, J Huang, V Rathod, N Johnston, A Rabinovich, K Murphy
arXiv preprint arXiv:1503.01558, 2015
Scale-Space Flow for End-to-End Optimized Video Compression
E Agustsson, D Minnen, N Johnston, J Balle, SJ Hwang, G Toderici
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Nonlinear transform coding
J Ballé, PA Chou, D Minnen, S Singh, N Johnston, E Agustsson, ...
IEEE Journal of Selected Topics in Signal Processing 15 (2), 339-353, 2020
Spatially adaptive image compression using a tiled deep network
D Minnen, G Toderici, M Covell, T Chinen, N Johnston, J Shor, SJ Hwang, ...
2017 IEEE International Conference on Image Processing (ICIP), 2796-2800, 2017
Computationally Efficient Neural Image Compression
N Johnston, E Eban, A Gordon, J Ballé
arXiv preprint arXiv:1912.08771, 2019
Integer networks for data compression with latent-variable models
J Ballé, N Johnston, D Minnen
International Conference on Learning Representations, 2018
End-to-end Learning of Compressible Features
S Singh, S Abu-El-Haija, N Johnston, J Ballé, A Shrivastava, G Toderici
2020 IEEE International Conference on Image Processing (ICIP), 3349-3353, 2020
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
M Covell, N Johnston, D Minnen, SJ Hwang, J Shor, S Singh, D Vincent, ...
arXiv preprint arXiv:1705.06687, 2017
Lvac: Learned volumetric attribute compression for point clouds using coordinate based networks
B Isik, P Chou, SJ Hwang, N Johnston, G Toderici
Frontiers in Signal Processing, 65, 2021
Towards A Semantic Perceptual Image Metric
T Chinen, J Ballé, C Gu, SJ Hwang, S Ioffe, N Johnston, T Leung, ...
2018 25th IEEE International Conference on Image Processing (ICIP), 624-628, 2018
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference
S Baluja, D Marwood, M Covell, N Johnston
arXiv preprint arXiv:1809.09244, 2018
Towards Generative Video Compression
F Mentzer, E Agustsson, J Ballé, D Minnen, N Johnston, G Toderici
arXiv preprint arXiv:2107.12038, 2021
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference
M Covell, D Marwood, S Baluja, N Johnston
arXiv preprint arXiv:1906.04798, 2019
Neural Video Compression Using GANs for Detail Synthesis and Propagation
F Mentzer, E Agustsson, J Ballé, D Minnen, N Johnston, G Toderici
European Conference on Computer Vision, 562-578, 2022
Learning to Render Better Image Previews
S Baluja, D Marwood, N Johnston, M Covell
2019 IEEE International Conference on Image Processing (ICIP), 1700-1704, 2019
The Need for Medically Aware Video Compression in Gastroenterology
J Shor, N Johnston
arXiv preprint arXiv:2211.01472, 2022
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