Generative Compression
release_mpkm5tvw7jdh5iyl3lblxps3ai
by
Shibani Santurkar, David Budden, Nir Shavit
2017
Abstract
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.
In text/plain
format
Archived Files and Locations
application/pdf 5.8 MB
file_qqq5ai3vhbbe3oirbhuxotcccy
|
arxiv.org (repository) web.archive.org (webarchive) |
1703.01467v2
access all versions, variants, and formats of this works (eg, pre-prints)