Fast Screen Content Coding in HEVC Using Machine Learning
Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new too...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/c27c3ecca20940ac9605b4044c058772 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new tools such as Intra Block Copy (IBC) and Palette (PLT) mode. These tools improve the coding efficiency, but they come with a huge computation complexity. In this paper, we propose a scheme to reduce the encoding time of SC encoder. It has two algorithms based on the Decision Rule (DR) machine learning technique. The first algorithm, which is called Mode Skipping (MS), is used to skip the unnecessary SC mode checking. Early Pruning Termination (EPT), which is the second algorithm, is used to stop the partitioning process early. A small number of features has to be calculated for the trained models. The proposed scheme was implemented then simulated by using the standard software test model SCM-6. The experimental results show that the time complexity is reduced by 37.84% on average while the Bjøntegaard delta bit-rate (BD-R) increases by 1.34% only. |
|---|