Entropy coding in image and video compression using machine learning
US10652581B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 27, 2019 |
| Grant date | May 12, 2020 |
| Priority date | — |
| Expiry date | Feb 27, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/70
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Machine learning is used to refine a probability distribution for entropy coding video or image data. A probability distribution is determined for symbols associated with a video block (e.g., quantized transform coefficients, such as during encoding, or syntax elements from a bitstream, such as during decoding), and a set of features is extracted from video data associated with the video block and/or neighbor blocks. The probability distribution and the set of features are then processed using machine learning to produce a refined probability distribution. The video data associated with a video block are entropy coded according to the refined probability distribution. Using machine learning to refine the probability distribution for entropy coding minimizes the cross-entropy loss between the symbols to entropy code and the refined probability distribution.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.