Generative Compression

03/04/2017
by   Shibani Santurkar, et al.
0

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length coding schemes.

READ FULL TEXT

page 1

page 3

page 5

page 7

page 8

research
11/28/2014

V-variable image compression

V-variable fractals, where V is a positive integer, are intuitively frac...
research
06/17/2022

Lossy Compression with Gaussian Diffusion

We describe a novel lossy compression approach called DiffC which is bas...
research
06/05/2021

Neural Distributed Source Coding

Distributed source coding is the task of encoding an input in the absenc...
research
05/03/2023

Toward Textual Transform Coding

Inspired by recent work on compression with and for young humans, the su...
research
12/28/2022

Latent Discretization for Continuous-time Sequence Compression

Neural compression offers a domain-agnostic approach to creating codecs ...
research
03/29/2022

Neural Face Video Compression using Multiple Views

Recent advances in deep generative models led to the development of neur...
research
12/20/2020

Learning to Localize Through Compressed Binary Maps

One of the main difficulties of scaling current localization systems to ...

Please sign up or login with your details

Forgot password? Click here to reset