Generative Compression
Name
1703.01467.pdf
Description
Submitted version
Size
5.54 MB
Format
Adobe PDF
Checksum (MD5)
db983eda108314620558739e89078230
Author(s)
Santurkar, Shibani (Shibani Vinay)
Budden, David
Shavit, Nir N.
Date Issued
September 2018
Journal
2018 Picture Coding Symposium (PCS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
S. Santurkar, D. Budden and N. Shavit, "Generative Compression," 2018 Picture Coding Symposium (PCS), San Francisco, CA, 2018, pp. 258-262, doi: 10.1109/PCS.2018.8456298.
Version
Original manuscript
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. 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 deeper compression levels for both image and video data. We also show that generative compression is orders- of-magnitude more robust to bit errors (e.g., from noisy channels) than traditional variable-length coding schemes.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/PCS.2018.8456298
