Novel Encoding Time Reduction Algorithms for the 3D-HEVC Inter-Frame Prediction
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This chapter presents the developed algorithms for encoding time reduction of the depth map inter-frame prediction. Three algorithms are presented along with their motivational analysis, where each algorithm actuate in a different encoding level. The first algorithm considered the simplicity of the depth maps and target Motion Estimation (ME) and Disparity Estimation (DE). The second proposal focuses on reusing the texture-encoded information for selecting a lower quantity of tools for being evaluated if these tools can obtain sound Rate-Distortion (RD) results. The last algorithm uses decision trees built with machine learning for pruning the quadtree evaluation when lower levels of quadtree are not required.
The experimental results regarding all algorithms are also presented in this chapter, and these results are compared to related works presented in Sect. 3.2. The algorithms presented in this chapter surpass other related works, presenting better results regarding encoding efficiency and timesaving.
Keywords3D-HEVC Depth maps coding Inter-frame prediction Encoding time reduction Machine learning Statistical analysis Motion estimation Block-level algorithm Data mining Performance-aware algorithms
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