DeepAI AI Chat
Log In Sign Up

Feedback Recurrent Autoencoder for Video Compression

by   Adam Golinski, et al.

Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems. Specifically, autoencoder based learned image or video compression solutions are emerging as strong competitors to traditional approaches. In this work, We propose a new network architecture, based on common and well studied components, for learned video compression operating in low latency mode. Our method yields state of the art MS-SSIM/rate performance on the high-resolution UVG dataset, among both learned video compression approaches and classical video compression methods (H.265 and H.264) in the rate range of interest for streaming applications. Additionally, we provide an analysis of existing approaches through the lens of their underlying probabilistic graphical models. Finally, we point out issues with temporal consistency and color shift observed in empirical evaluation, and suggest directions forward to alleviate those.


page 12

page 18

page 19

page 23

page 24

page 28

page 29


Insights from Generative Modeling for Neural Video Compression

While recent machine learning research has revealed connections between ...

Feedback Recurrent AutoEncoder

In this work, we propose a new recurrent autoencoder architecture, terme...

Neural Weight Step Video Compression

A variety of compression methods based on encoding images as weights of ...

Video Compression With Rate-Distortion Autoencoders

In this paper we present a a deep generative model for lossy video compr...

ELF-VC: Efficient Learned Flexible-Rate Video Coding

While learned video codecs have demonstrated great promise, they have ye...

Deep Generative Models for Distribution-Preserving Lossy Compression

We propose and study the problem of distribution-preserving lossy compre...

CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

This paper presents CompressAI, a platform that provides custom operatio...