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Johannes Ballé
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2020 – today
- 2024
- [j6]Ezgi Özyilkan
, Johannes Ballé
Neural Distributed Compressor Discovers Binning. IEEE J. Sel. Areas Inf. Theory 5: 246-260 (2024) - [c33]Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis:
Wasserstein Distortion: Unifying Fidelity and Realism. CISS 2024: 1-6 - [c32]Daniel Severo, Lucas Theis, Johannes Ballé:
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. ICLR 2024 - [c31]Alfredo De la Fuente, Saurabh Singh, Johannes Ballé:
Fourier Basis Density Model. PCS 2024: 1-5 - [i25]Alfredo De la Fuente, Saurabh Singh, Johannes Ballé:
Fourier Basis Density Model. CoRR abs/2402.15345 (2024) - 2023
- [j5]Sebastià Mijares i Verdú
A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning. Remote. Sens. 15(18): 4422 (2023) - [c30]Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný:
A Rate-Distortion View on Model Updates. Tiny Papers @ ICLR 2023 - [c29]Ezgi Özyilkan, Johannes Ballé, Elza Erkip:
Learned Wyner-Ziv Compressors Recover Binning. ISIT 2023: 701-706 - [i24]Ezgi Özyilkan, Johannes Ballé, Elza Erkip:
Learned Wyner-Ziv Compressors Recover Binning. CoRR abs/2305.04380 (2023) - [i23]Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis:
Wasserstein Distortion: Unifying Fidelity and Realism. CoRR abs/2310.03629 (2023) - [i22]Daniel Severo, Lucas Theis, Johannes Ballé:
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. CoRR abs/2310.05986 (2023) - [i21]Ezgi Özyilkan, Johannes Ballé, Elza Erkip:
Neural Distributed Compressor Discovers Binning. CoRR abs/2310.16961 (2023) - 2022
- [c28]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. AISTATS 2022: 7680-7723 - [c27]Sebastià Mijares i Verdú, Johannes Ballé, Valero Laparra, Joan Bartrina-Rapesta
Hyperspectral remote sensing data compression with neural networks. DCC 2022: 476 - [c26]Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici:
Neural Video Compression Using GANs for Detail Synthesis and Propagation. ECCV (26) 2022: 562-578 - [c25]Alexander Hepburn, Valero Laparra, Raúl Santos-Rodríguez, Johannes Ballé, Jesus Malo:
On the relation between statistical learning and perceptual distances. ICLR 2022 - [c24]Sourbh Bhadane, Aaron B. Wagner, Johannes Ballé:
Do Neural Networks Compress Manifolds Optimally? ITW 2022: 582-587 - [i20]Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný:
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. CoRR abs/2201.02664 (2022) - [i19]Sourbh Bhadane, Aaron B. Wagner, Johannes Ballé:
Do Neural Networks Compress Manifolds Optimally? CoRR abs/2205.08518 (2022) - 2021
- [j4]Johannes Ballé
Nonlinear Transform Coding. IEEE J. Sel. Top. Signal Process. 15(2): 339-353 (2021) - [c23]Aaron B. Wagner, Johannes Ballé:
Neural Networks Optimally Compress the Sawbridge. DCC 2021: 143-152 - [c22]Thomas Bird, Johannes Ballé, Saurabh Singh, Philip A. Chou:
3D Scene Compression through Entropy Penalized Neural Representation Functions. PCS 2021: 1-5 - [i18]Thomas Bird, Johannes Ballé, Saurabh Singh, Philip A. Chou:
3D Scene Compression through Entropy Penalized Neural Representation Functions. CoRR abs/2104.12456 (2021) - [i17]Alexander Hepburn, Valero Laparra, Raúl Santos-Rodríguez, Johannes Ballé, Jesús Malo:
On the relation between statistical learning and perceptual distances. CoRR abs/2106.04427 (2021) - [i16]Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici:
Towards Generative Video Compression. CoRR abs/2107.12038 (2021) - [i15]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. CoRR abs/2111.00092 (2021) - 2020
- [c21]Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Ballé, Sung Jin Hwang, George Toderici:
Scale-Space Flow for End-to-End Optimized Video Compression. CVPR 2020: 8500-8509 - [c20]Saurabh Singh, Sami Abu-El-Haija, Nick Johnston, Johannes Ballé, Abhinav Shrivastava, George Toderici:
End-to-End Learning of Compressible Features. ICIP 2020: 3349-3353 - [c19]Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava:
Scalable Model Compression by Entropy Penalized Reparameterization. ICLR 2020 - [c18]Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy T. Chinen:
An Unsupervised Information-Theoretic Perceptual Quality Metric. NeurIPS 2020 - [i14]Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy T. Chinen:
An Unsupervised Information-Theoretic Perceptual Quality Metric. CoRR abs/2006.06752 (2020) - [i13]Johannes Ballé, Philip A. Chou, David Minnen, Saurabh Singh, Nick Johnston, Eirikur Agustsson, Sung Jin Hwang, George Toderici:
Nonlinear Transform Coding. CoRR abs/2007.03034 (2020) - [i12]Saurabh Singh, Sami Abu-El-Haija, Nick Johnston, Johannes Ballé, Abhinav Shrivastava, George Toderici:
End-to-end Learning of Compressible Features. CoRR abs/2007.11797 (2020) - [i11]Aaron B. Wagner, Johannes Ballé:
Neural Networks Optimally Compress the Sawbridge. CoRR abs/2011.05065 (2020)
2010 – 2019
- 2019
- [c17]Johannes Ballé, Nick Johnston, David Minnen:
Integer Networks for Data Compression with Latent-Variable Models. ICLR (Poster) 2019 - [i10]Jasmine Collins, Johannes Ballé, Jonathon Shlens:
Accelerating Training of Deep Neural Networks with a Standardization Loss. CoRR abs/1903.00925 (2019) - [i9]Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava:
Model Compression by Entropy Penalized Reparameterization. CoRR abs/1906.06624 (2019) - [i8]Nick Johnston, Elad Eban, Ariel Gordon, Johannes Ballé:
Computationally Efficient Neural Image Compression. CoRR abs/1912.08771 (2019) - 2018
- [c16]Troy T. Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean M. O'Malley, Charles Rosenberg, George Toderici:
Towards A Semantic Perceptual Image Metric. ICIP 2018: 624-628 - [c15]Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston:
Variational image compression with a scale hyperprior. ICLR (Poster) 2018 - [c14]David Minnen, Johannes Ballé, George Toderici:
Joint Autoregressive and Hierarchical Priors for Learned Image Compression. NeurIPS 2018: 10794-10803 - [c13]Johannes Ballé:
Efficient Nonlinear Transforms for Lossy Image Compression. PCS 2018: 248-252 - [i7]Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston:
Variational image compression with a scale hyperprior. CoRR abs/1802.01436 (2018) - [i6]Troy T. Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean M. O'Malley, Charles Rosenberg, George Toderici:
Towards a Semantic Perceptual Image Metric. CoRR abs/1808.00447 (2018) - [i5]David Minnen, Johannes Ballé, George Toderici:
Joint Autoregressive and Hierarchical Priors for Learned Image Compression. CoRR abs/1809.02736 (2018) - 2017
- [c12]Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
End-to-end Optimized Image Compression. ICLR 2017 - [c11]Alexander Berardino, Valero Laparra, Johannes Ballé, Eero P. Simoncelli:
Eigen-Distortions of Hierarchical Representations. NIPS 2017: 3530-3539 - [i4]Valero Laparra, Alexander Berardino, Johannes Ballé, Eero P. Simoncelli:
Perceptually Optimized Image Rendering. CoRR abs/1701.06641 (2017) - [i3]Alexander Berardino, Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
Eigen-Distortions of Hierarchical Representations. CoRR abs/1710.02266 (2017) - 2016
- [c10]Valero Laparra, Johannes Ballé, Alexander Berardino, Eero P. Simoncelli
Perceptual image quality assessment using a normalized Laplacian pyramid. HVEI 2016: 1-6 - [c9]Johannes Ballé, Valero Laparra
End-to-end optimization of nonlinear transform codes for perceptual quality. PCS 2016: 1-5 - [c8]Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
Density Modeling of Images using a Generalized Normalization Transformation. ICLR 2016 - [i2]Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
End-to-end optimization of nonlinear transform codes for perceptual quality. CoRR abs/1607.05006 (2016) - [i1]Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
End-to-end Optimized Image Compression. CoRR abs/1611.01704 (2016) - 2015
- [c7]Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz, Eero P. Simoncelli:
The local low-dimensionality of natural images. ICLR 2015 - 2014
- [c6]Johannes Ballé, Eero P. Simoncelli
Learning sparse filter bank transforms with convolutional ICA. ICIP 2014: 4013-4017 - 2012
- [b1]Johannes Ballé:
Image compression by microtexture synthesis. RWTH Aachen University, Shaker 2012, ISBN 978-3-8440-1449-5, pp. 1-136 - [j3]David Miraut
Efficient shift-variant image restoration using deformable filtering (Part II): PSF field estimation. EURASIP J. Adv. Signal Process. 2012: 193 (2012) - [c5]Fabian Jäger, Johannes Ballé:
Median trilateral loop filter for depth map video coding. PCS 2012: 61-64 - [c4]Johannes Ballé:
Subjective evaluation of texture similarity metrics for compression applications. PCS 2012: 241-244 - 2011
- [j2]Johannes Ballé, Aleksandar Stojanovic, Jens-Rainer Ohm:
Models for Static and Dynamic Texture Synthesis in Image and Video Compression. IEEE J. Sel. Top. Signal Process. 5(7): 1353-1365 (2011) - [c3]Christian Feldmann, Johannes Ballé:
Improved entropy coding for component-based image coding. ICIP 2011: 325-328 - 2010
- [j1]Alexander Behrens, Linus Atorf, Robert Schwann, Bernd Neumann, Rainer Schnitzler, Johannes Ballé, Thomas Herold, Aulis Telle, Tobias G. Noll, Kay Hameyer, Til Aach:
MATLAB Meets LEGO Mindstorms - A Freshman Introduction Course Into Practical Engineering. IEEE Trans. Educ. 53(2): 306-317 (2010)
2000 – 2009
- 2009
- [c2]Johannes Ballé, Bastian Jurczyk, Aleksandar Stojanovic:
Component-based image coding using non-local means filtering and an autoregressive texture model. ICIP 2009: 1937-1940 - 2007
- [c1]Johannes Ballé, Mathias Wien:
Extended Texture Prediction for H.264/AVC Intra Coding. ICIP (6) 2007: 93-96
Coauthor Index
aka: Nick Johnston
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last updated on 2024-08-08 20:16 CEST by the dblp team
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