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Found 19 papers, 9 papers with code
Sample what you cant compress
Our work combines autoencoder representation learning with diffusion and is, to our knowledge, the first to demonstrate the efficacy of jointly learning a continuous encoder and decoder under a diffusion-based loss.
VideoPoet: A Large Language Model for Zero-Shot Video Generation
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals.
Ranked #4 on
Text-to-Video Generation
on MSR-VTT
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation.
Ranked #2 on
Video Prediction
on Kinetics-600 12 frames, 64x64
Finite Scalar Quantization: VQ-VAE Made Simple
Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets.
Advancing The Rate-Distortion-Computation Frontier For Neural Image Compression
The rate-distortion performance of neural image compression models has exceeded the state-of-the-art for non-learned codecs, but neural codecs are still far from widespread deployment and adoption.
Multi-Realism Image Compression with a Conditional Generator
By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models.
VCT: A Video Compression Transformer
The resulting video compression transformer outperforms previous methods on standard video compression data sets.
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.
Channel-wise Autoregressive Entropy Models for Learned Image Compression
In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective.
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.
Image-Dependent Local Entropy Models for Learned Image Compression
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion performance.
Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
Variational image compression with a scale hyperprior
We describe an end-to-end trainable model for image compression based on variational autoencoders.
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Full Resolution Image Compression with Recurrent Neural Networks
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Variable Rate Image Compression with Recurrent Neural Networks
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements.
