Method for initial quantization parameter optimization in video coding
US10560696B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 25, 2018 |
| Grant date | Feb 11, 2020 |
| Priority date | — |
| Expiry date | Aug 9, 2038 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02T10/40
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A machine learning based initial quantization parameter (QP) prediction method, which can effectively optimize RC performance A machine learning framework for initial QP prediction is proposed, where learning labels are built with the criterion of maximizing rate-distortion (RC) performance, which is proved to be much more effective than the QP determination method with the only consideration on sum of the absolute transformed difference (SATD) complexity. Instead of target bits per pixel for intra frame, target bits per pixel for remaining frames is used as sample data to avoid empirically setting intra frame bit allocation, thus improve the prediction accuracy as the real-time updated remaining bits can better reflect the real-time requirements on the level of QPs. In addition, a clipping and decision approach based on the previous initial QP and the target bits per pixel for all remaining frames is proposed, which can help fast QP adaption and quality smoothness.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.