Name
Abstract | ||
|---|---|---|
The emergent 3D High Efficiency Video Coding (3D-HEVC) is an extension of the High Efficiency Video Coding (HEVC) standard for the compression of the multi-view texture videos plus depth maps format. Since depth maps have different statistical properties compared to texture video, various new intra tools have been added to 3D-HEVC depth coding. In current 3D-HEVC, new intra tools are utilized together with the conventional HEVC intra prediction modes for depth coding. This technique achieves the highest possible coding efficiency, but leads to an extremely high computational complexity which limits 3D-HEVC from practical applications. In this paper, we propose a fast intra mode decision algorithm for depth coding in 3D-HEVC. The basic idea of the proposed algorithm is to utilize the depth map characteristics to predict the current depth prediction mode and skip some specific depth intra modes rarely used in 3D-HEVC depth coding. Based on this analysis, two fast intra mode decision strategies are proposed including reduction of the number of conventional intra prediction modes, and simplification of depth modeling modes (DMMs). Experimental results demonstrate that the proposed algorithm can save 30 % coding runtime on average while maintaining almost the same rate-distortion (RD) performance as the original 3D-HEVC encoder. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s11045-016-0388-1 | Multidim. Syst. Sign. Process. |
Keywords | Field | DocType |
3D-HEVC,Depth map,Mode decision,Intra coding | Edge detection,Computer science,Coding (social sciences),Artificial intelligence,Depth map,Computer vision,Mathematical optimization,Algorithmic efficiency,Coding tree unit,Algorithm,Depth map coding,Encoder,Computational complexity theory | Journal |
Volume | Issue | ISSN |
28 | 4 | 0923-6082 |
Citations | PageRank | References |
8 | 0.51 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuwen Zhang | 1 | 71 | 16.24 |
| Yongshuang Yang | 2 | 8 | 0.51 |
| Hua-wen Chang | 3 | 49 | 2.23 |
| Zhang, Weiwei | 4 | 52 | 6.01 |
| Yong Gan | 5 | 68 | 5.01 |