Name
Abstract | ||
|---|---|---|
The high efficiency video coding (HEVC) standard provides superior efficiency for encoding and improves the compression ratio by almost 50% compared with previous video coding standards, such as advanced video coding (AVC). However, more intensive computation complexity is introduced by implementing the flexible quad tree-structured coding model. In a typical HEVC encoder, coding units (CUs) in a coding tree unit (CTU) that is built as a quad-tree structure are recursively traversed each depth level (CU size) to select the optimal coding configuration. Therefore, most of the encoding time is spent searching for the optimal coding configuration. In this paper, an efficient and fast CU size decision algorithm is proposed to reduce HEVC encoder complexity by the spatiotemporal features. First, an adaptive depth-range prediction method minimizes the possible range depth level by observing previous frames and proximal CTUs. Second, an early termination method based on the boundary examination from the de-blocking filter (DBF) prevents unnecessary calculation on small CU sizes. Furthermore, according to the sum of absolution difference (SAD), a smooth area detection mechanism is triggered when the predictive depth range excludes the largest CU size. This mechanism increase the bitrate of the CU, which contains static objects with complex textures. Compared with the HM 16, the experimental results revealed that the proposed algorithm can achieve an average 59.73% and 64.98% reduction in encoding time along with a 0.68% and 1.27% Bjontegaard Delta bitrate (BDBR) penalty for various test videos under low-delay P and random-access conditions, respectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TBC.2019.2960938 | IEEE Transactions on Broadcasting |
Keywords | DocType | Volume |
HEVC,CU size decision | Journal | 66 |
Issue | ISSN | Citations |
1 | 0018-9316 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao-Tsung Kuo | 1 | 4 | 1.16 |
| Pei-Yin Chen | 2 | 314 | 38.47 |
| Hong-Cheng Lin | 3 | 1 | 0.35 |