Deep learning based adaptive arithmetic coding and codelength regularization
US10748062B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 22, 2017 |
| Grant date | Aug 18, 2020 |
| Priority date | — |
| Expiry date | Nov 6, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/91
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.