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Found 24 papers, 8 papers with code
Fourier Basis Density Model
We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis.
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
We show how perceptual embeddings of the visual system can be constructed at inference-time with no training data or deep neural network features.
Wasserstein Distortion: Unifying Fidelity and Realism
We introduce a distortion measure for images, Wasserstein distortion, that simultaneously generalizes pixel-level fidelity on the one hand and realism or perceptual quality on the other.
Do Neural Networks Compress Manifolds Optimally?
Artificial Neural-Network-based (ANN-based) lossy compressors have recently obtained striking results on several sources.
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server.
Neural Video Compression using GANs for Detail Synthesis and Propagation
Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods.
On the relation between statistical learning and perceptual distances
Since machine learning relies on the statistics of training data as well, the above connection has interesting implications when using perceptual distances (which mimic the behavior of the human visual system) as a loss function.
3D Scene Compression through Entropy Penalized Neural Representation Functions
We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints.
End-to-end Learning of Compressible Features
We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.
An Unsupervised Information-Theoretic Perceptual Quality Metric
We show that PIM is competitive with supervised metrics on the recent and challenging BAPPS image quality assessment dataset and outperforms them in predicting the ranking of image compression methods in CLIC 2020.
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).
Scalable Model Compression by Entropy Penalized Reparameterization
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 a reparameterization.
Integer Networks for Data Compression with Latent-Variable Models
We consider the problem of using variational latent-variable models for data compression.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
Variational image compression with a scale hyperprior
We describe an end-to-end trainable model for image compression based on variational autoencoders.
Efficient Nonlinear Transforms for Lossy Image Compression
We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN.
Eigen-Distortions of Hierarchical Representations
We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans.
Perceptually Optimized Image Rendering
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 original scene.
End-to-end Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.
End-to-end optimization of nonlinear transform codes for perceptual quality
We introduce a general framework for end-to-end optimization of the rate--distortion performance of nonlinear transform codes assuming scalar quantization.
Density Modeling of Images using a Generalized Normalization Transformation
The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant.
A model of sensory neural responses in the presence of unknown modulatory inputs
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability.
The local low-dimensionality of natural images
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 covariance that varies slowly over spatial position.
