1.4k citations · 3.5k across the 13 of their papers we have counts for
14 papers
Learned Wyner-Ziv Compressors Recover Binning
Ezgi Ozyilkan, Johannes Ballé, Elza Erkip
We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case…
Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah, Wei-Ning Chen, Johannes Balle +2
Compressing the output of ε-locally differentially private (LDP) randomizers naively leads to suboptimal utility. In this work, we demonstrate the benefits of using schemes that jo…
Scalable Model Compression by Entropy Penalized Reparameterization
Deniz Oktay, Johannes Ballé, Saurabh Singh +1
We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a "latent" space, amounting to…
Accelerating Training of Deep Neural Networks with a Standardization Loss
Jasmine Collins, Johannes Balle, Jonathon Shlens
A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better…
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George Toderici
Recent models for learned image compression are based on autoencoders, learning approximately invertible mappings from pixels to a quantized latent representation. These are combin…
Towards a Semantic Perceptual Image Metric
Troy Chinen, Johannes Ballé, Chunhui Gu +8
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 14…
Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh +2
We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies i…
Efficient Nonlinear Transforms for Lossy Image Compression
Johannes Ballé
We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN. Both techniques have been successfully used…
Eigen-Distortions of Hierarchical Representations
Alexander Berardino, Johannes Ballé, Valero Laparra +1
We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans. Specifically, we utilize Fisher informat…
Perceptually Optimized Image Rendering
Valero Laparra, Alex Berardino, Johannes Ballé +1
We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the origin…
End-to-end Optimized Image Compression
Johannes Ballé, Valero Laparra, Eero P. Simoncelli
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constr…
End-to-end optimization of nonlinear transform codes for perceptual quality
Johannes Ballé, Valero Laparra, Eero P. Simoncelli
We introduce a general framework for end-to-end optimization of the rate--distortion performance of nonlinear transform codes assuming scalar quantization. The framework can be use…
Density Modeling of Images using a Generalized Normalization Transformation
Johannes Ballé, Valero Laparra, Eero P. Simoncelli
We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then nor…
The local low-dimensionality of natural images
Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz +1
We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covaria…