Abstract
To improve the parallel processing capability of video coding, the emerging high efficiency video coding (HEVC) standard introduces two parallel techniques, i.e., Wavefront Parallel Processing (WPP) and Tiles, to make it much more parallel-friendly than its predecessors. However, these two techniques are designed to explore coarse-grained parallelism in HEVC encoding on multicore Central Processing Unit (CPU) platforms. As the computing architecture undergoes a trend toward heterogeneity in the last decade, multi-grained parallel computing methods can be designed to accelerate HEVC encoding on heterogeneous systems. In this paper, a multi-grained parallel solution (MPS) is proposed to optimize HEVC encoding on a typical heterogeneous platform. A massively parallel motion estimation algorithm is employed by MPS to parallelize part of HEVC encoding on Graphic Processing Unit (GPU). Meanwhile, several other HEVC encoding modules are accelerated on CPU through the cooperation of WPP and an adaptive parallel mode decision algorithm. The parallelism between CPU and GPU is well designed and implemented to guarantee an efficient concurrent execution of HEVC encoding on multi-grained parallel levels. The effectiveness of the proposed MPS for HEVC encoding is verified on a number of experiments.
| Original language | English |
|---|---|
| Pages (from-to) | 2997-3009 |
| Journal | IEEE Transactions on Multimedia |
| Volume | 21 |
| Issue number | 12 |
| Early online date | 13 May 2019 |
| DOIs | |
| Publication status | Published - Dec 2019 |
| Externally published | Yes |
Funding
This work was supported in part by National Natural Science Foundation of China under Grant 61622115 and Grant 61472281, in part by National Key R&D Program of China (2017YFB1401404), and in part by Shanghai Engineering Research Center of Industrial Vision Perception and Intelligent Computing (17DZ2251600).
Keywords
- GPU
- heterogeneous computing
- HEVC
- parallel computing
- Video coding