Using generative adversarial networks in compression
US11315011B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 15, 2017 |
| Grant date | Apr 26, 2022 |
| Priority date | — |
| Expiry date | Feb 1, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/91
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.